AI & Machine Learning

What Google Cloud announced in AI this month

Thu, 30 Apr 2026 16:00:00 +0000

Editor’s note: Want to keep up with the latest from Google Cloud? Check back here for a monthly recap of our latest updates, announcements, resources, events, learning opportunities, and more.

We hosted Google Cloud Next in Las Vegas on April 22, announcing incredible innovations from Gemini Enterprise Agent Platform to our eight-generation TPUs. We also expanded the Gemini Enterprise app in collaborative ways – now, with new features like Projects, you can work side-by-side with your agents and colleagues.

If you missed the livestream, take a look at our Day 1 recap. It’s been incredible to see how customers have been applying AI in thousands of ways — so far, we’ve counted more than 1,300 examples.

Top announcements

1. Gemini Enterprise Agent Platform: Our new, comprehensive platform to build, scale, govern, and optimize agents. Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.

The platform is designed around four core pillars — build, scale, govern, and optimize — that allow teams to collaborate seamlessly. Learn more about Agent Platform here.

2. Gemini Enterprise app has all the key components to let teams discover, create, share, and run AI agents in a single environment. At Next ‘26, we introduced several new capabilities in the Gemini Enterprise app:

Agent Designer uses the same no-code agent designer experience of Agent Platform and lets employees build sophisticated schedule- and trigger-based agents using any enterprise connector. It gives you a virtual flowchart of your agent, allowing you to inspect, test, and approve workflows, ensuring total transparency for executing critical business processes.
Long-running agents are designed to execute complex business processes. They can work autonomously in secure cloud sandboxes, giving agents the ability to orchestrate business logic, write code to build custom tools, and complete multi-step work like reconciliation activities or sales prospect sequencing — without needing constant prompting.
Inbox in Gemini Enterprise provides a central location to monitor, guide, and help manage all of your agent activity, including your long-running agents. Notifications are intuitively categorized into actionable groups like "Needs your input," "Errors," and "Completed.”
Projects create a dedicated space where the agent’s memory is confined to the files and conversations your team adds. By connecting it to data sources including Google Drive, NotebookLM, and Google Group Chats, the agent becomes an expert on a specific topic and can provide team members daily briefings or status updates without digging through months of documents.
Skills create simple shortcuts using an “@” mention for repetitive tasks such as applying brand guidelines, formatting a report, and accessing specific data.
Canvas gives our customers an interactive editor directly within Gemini Enterprise. It allows teams to easily create and edit Docs and Slides, and even export to Microsoft 365 files, within the same experience.
Agent Gallery provides access to third-party agents from partners like Adobe, Atlassian, Lovable, and ServiceNow, and is adding more third-party connectors for Asana, Mailchimp, Workday, and more. These integrations enable your agents to retrieve data and execute tasks with your systems-of-record.

3. AI Hypercomputer: Designed specifically for demanding AI workloads, our AI Hypercomputer is an advanced, purpose-built architecture that unites performance-optimized hardware for compute, storage, networking, open software and machine learning frameworks — as well as flexible consumption models — into a single, integrated system. We are announcing innovations at every layer of the AI Hypercomputer:

TPU 8t, optimized for training, uses breakthrough Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 PB of shared, high-bandwidth memory in a single superpod. It achieves 3x the processing power of Ironwood and delivers up to 2x more performance/Watt.
TPU 8i, optimized for inference, uses our new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features 3x more on-chip SRAM compared to previous versions to host larger KV caches entirely on-silicon and integrates a specialized Collectives Acceleration Engine. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, enabling millions of concurrent agents to run cost-effectively.

4. The Agentic Data Cloud: A new data architecture built for the speed and scale of agentic AI. The Agentic Data Cloud delivers an AI-native architecture, allowing agents to perceive, reason, and act on your behalf in real-time, including:

Cross-Cloud Lakehouse, standardized on Apache Iceberg, is our Lakehouse that enables you to leave your data in AWS or Azure (coming later this year) while querying it instantly — without the friction of vendor lock-in or the cost of data movement
Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Then our Knowledge Engine uses Gemini to autonomously tag, define logic and instantly map complex relationships across your entire enterprise, providing the semantic definition your agents have been missing.

5. Protecting the agentic enterprise: Security built for the AI era. Our full-stack AI approach, from the chips to the models, gives you a competitive advantage with better integration and velocity to help protect customers. Not only can Google action insights from the world’s largest threat observatory and Mandiant frontline experts, but we also bring cutting-edge insights and breakthroughs from Google DeepMind, to help make your platforms more secure.

Agentic defense: Three new agents in Google Security Operations can help hunt threats, engineer detections, and provide context on third parties. You can build your own security agents with remote Google Cloud model context protocol (MCP) server support for Google Security Operations, now generally available. You can also access the MCP server client directly from the Google Security Operations chat interface, available in preview.
Protecting AI and cloud apps across any infrastructure with Wiz: Newly expanded AI coverage helps build secure agents across clouds and AI studios. New AI-Bill of Materials in development tools can help secure AI-generated code and mitigate the risk of shadow AI. Learn more.
Securing agents and the agentic web: Model Armor can integrate with Agent Gateway, and new Agent Identities provide more layers of defense against shadow AI. Google Cloud Fraud Defense, the next evolution of reCAPTCHA, offers agent-specific capabilities that can help secure the agentic web as well as the entire user and customer journey.
Trusted Cloud: We’re simplifying permissions with modern IAM, and advancing Google Cloud security with new capabilities in Security Command Center plus new innovations in data and network security.
New partner-supported workflows for Google Security Operations: This new robust cohort of partner integrations includes partners developing their own agentic security operations centers (SOCs).

You can catch up on all our security announcements from Next ‘26 here.

News you can use

Guide to prompting Gemini 3.1 Flash TTS (text-to-speech): The new TTS model introduces a high level of controllability by allowing you to steer the delivery using more than 200 audio tags. We'll share how to get strong results from the model, whether you are building accessible gaming soundtracks, banking systems, or audiobooks. Learn more about the model here.
Ultimate prompting guide for Lyria 3 models: Lyria 3, Google's family of music-generation models, is designed to give you granular control over vocals, instrumentation, and arrangement. So we spent weeks testing against every musical genre and use case we could imagine. We put together this guide to share exactly what we learned and how you can get the best results.
How to find the sweet spot between cost and performance: This guide will walk you through Google Cloud's flexible gen AI infrastructure options, showing you how to find that sweet spot on the efficient frontier between cost and performance. We'll start with the foundational pay-as-you-go (PayGo) models and then explore how to layer on more specialized options to build a robust and cost-effective gen AI strategy.
Essential AI and cloud security now on by default: To support the next generation of AI innovators, we are offering on by default essential AI security and cloud security in Security Command Center Standard.
Securing AI inference on GKE with Model Armor: Here’s how to secure AI inference on Google Kubernetes Engine with Model Armor and high-performance storage.
Cloud CISO Perspectives: AI, security, and the workforce of the future: You can’t bring traditional security to an AI fight, so how do we defend against AI-powered attacks, boost defenders with AI, and secure AI use? Drop in on this RSA Conference fireside chat between Francis deSouza, Google Cloud COO and President, Security Products, and Nick Godfrey, senior director, Office of the CISO.

Stay tuned for monthly updates on Google Cloud’s AI announcements, news, and best practices. For a deeper dive into the latest from Google Cloud customers, read our monthly recap, Cool stuff customers built.

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March

March was a busy month for our AI teams. We launched Gemini Embedding 2, rolled out a highly cost-effective Veo 3.1 Lite model, and officially welcomed the Wiz team to Google Cloud to help redefine security in the AI era.

Alongside these launches, we created comprehensive guides to help you get the most out of these models, from prompting formulas for Nano Banana 2, to practical advice for optimizing your TPU training. Here’s a quick look at the latest news and resources to help your team build what’s next.

Top hits:

Gemini Embedding 2: Our first natively multimodal embedding model: Gemini Embedding 2 is our first natively multimodal embedding model that maps text, images, video, audio and documents into a single embedding space, enabling multimodal retrieval and classification across different types of media — and it’s available now in public preview.
Build with Veo 3.1 Lite, our most cost-effective video generation model: This model empowers developers to build high-volume video applications, at less than 50% of the cost of Veo 3.1 Fast, but with the same speed. This rounds out the Veo 3.1 model family, giving developers flexibility based on needs. For Cloud customers, it’s now available on Vertex AI.

Here’s a fun bonus: Check out our ultimate prompting guide for Veo 3.1 to get started.

Welcoming Wiz to Google Cloud: Redefining security for the AI era: Google has completed its acquisition of Wiz, a leading cloud and AI security platform. The Wiz team will join Google Cloud, and we will retain the Wiz brand. With the addition of Wiz, we will provide customers with a comprehensive platform to secure their cloud and hybrid environments, as well as accelerate threat prevention, detection, and response.
Gemini 3.1 Flash Live: Making audio AI more natural and reliable: We’ve improved 3.1 Flash Live’s overall quality, making it more reliable for developers and enterprises to build voice-first agents that can complete complex tasks at scale. On ComplexFuncBench Audio, a benchmark that captures multi-step function calling with various constraints, it leads with a score of 90.8% compared to our previous model.

News you can use:

The ultimate Nano Banana prompting guide: This is a must-read for anyone working with Nano Banana. We spent weeks testing Nano Banana 2 and Nano Banana Pro against every use case we could imagine to test its limits. We put together this guide to share exactly what we learned and how you can get the best results. Here’s an example formula: [Reference images] + [Relationship instruction] + [New scenario]

A developer’s guide to training with Ironwood TPUs: In this guide, we hear from Lillian Yu, CPA, CA , Product Strategy and Operation, and Liat Berry, Product Manager, on five strategies within the JAX and MaxText ecosystems designed to help developers refine training efficiency and hit peak performance on Ironwood hardware.
How to build production-ready AI agents with Google-managed MCP servers: In this guide, we anchor on a specific example. Cityscape is a demo agent built with Google's Application Development Kit (ADK) that turns a simple text prompt — like "Generate a cityscape for Kyoto" — into a unique, AI-generated city image. Check out the guide to learn more.

February

In February, we’re giving developers more reasoning power with Gemini 3.1 Pro and Claude 4.6, and faster creative scaling with Nano Banana 2. We’re also opening up new training programs and step-by-step guides to help you tackle the hardest parts of the AI lifecycle, from capacity planning to mounting defenses against AI-powered attacks.

Here’s a rundown of our latest news, tools, and resources to help you build what’s next.

Top hits

Pro-level image generation gets faster and more accessible with Nano Banana 2: To build creative that stands out, you need models that naturally integrate into your workflows and scale with ease. Check out our blog to see how this comes to life (and how customers are putting the model to work).

Introducing Gemini 3.1 Pro on Google Cloud: Gemini 3.1 Pro is a clear step forward in reasoning, designed to solve tougher problems, giving you the reasoning depth your business needs. Gemini 3.1 Pro is available starting today in preview in Vertex AI and Gemini Enterprise. Developers can access the model in preview via the Gemini API in Google AI Studio, Android Studio, Google Antigravity, and Gemini CLI.
Announcing Claude Opus 4.6 and Claude Sonnet 4.6 on Vertex AI: Now generally available on Vertex AI, explore our sample notebook to get started and visit our documentation for comprehensive pricing and regional availability details.
New AI threats report: Distillation, experimentation, and integration: John Hultquist, chief analyst, Google Threat Intelligence Group, details what security leaders should know from our newest AI threat report on experimentation, integration, and distillation attacks.

News you can use

A developer's guide to production-ready AI agents: To help developers work through these challenges, we've published a collection of guides covering the full agent lifecycle. These resources first appeared during Kaggle’s 5 days of AI Agents Intensive, and they’ve proven so popular and useful, we wanted to make sure a wider audience had access, as well.
Gemini Enterprise Agent Ready (GEAR) program now available: We opened the Gemini Enterprise Agent Ready (GEAR) learning program to everyone. As a new specialized pathway within the Google Developer Program, GEAR empowers developers and pros to build and deploy enterprise-grade agents with Google AI.
Your guide to Provisioned Throughput (PT) on Vertex AI: Check out this deep-dive blog designed to show you the resources available to you today on Vertex AI, and how you can get started capacity planning.
How AI can boost defenders, from defense in depth to the cyber kill chain (Q&A): We know that defenders are also developing powerful AI tools, but what’s still unknown is what it could mean for enterprise software ownership if companies have to constantly mount AI-directed defenses at AI-powered attacks?

Janurary

We used to have to learn the language of computers. In 2026, they’re learning ours.

We kicked off the year by exploring the future of agentic commerce, where AI agents navigate the web to find and buy products for us. Our leaders call this the "invisible shelf" — a world where commerce isn't tied to a specific website. To make this reality scalable, we announced the Universal Commerce Protocol (UCP), a shared language that allows agents and retailers to understand each other.

We brought that same fluency to our creative and technical tools:

Updates to Veo 3.1 allow creators to use simple inputs — like reference images — to generate precise, mobile-ready video.
Natural language queries: With Comments to SQL in BigQuery, we’re removing the language barrier to data. Engineers can now write queries by describing their intent in natural language, prioritizing the question over the code.

Let’s dive in.

Top hits

1. Gemini Enterprise for Customer Experience (CX): Specifically built for agentic retail, this platform transforms fragmented search, commerce and service touch points into one seamless journey — whether you need a shopping assistant, a support bot, agentic search or help with merchandising.

2. We announced Universal Commerce Protocol (UCP): A new open standard for agentic commerce that works across the entire shopping journey — from discovery and buying to post-purchase support. UCP establishes a common language for agents and systems to operate together across consumer surfaces, businesses and payment providers. So instead of requiring unique connections for every individual agent, UCP enables all agents to interact easily. UCP is built to work across verticals and is compatible with existing industry protocols like Agent2Agent (A2A), Agent Payments Protocol (AP2) and Model Context Protocol (MCP).

3. We updated Veo 3.1, including improvements to Ingredients to Video and Portrait mode: Veo is getting more expressive, with improvements that help you create more fun, creative, high-quality videos based on ingredient images, built directly for the mobile format. This includes:

Improvements to Veo 3.1 Ingredients to Video, our capability that lets you create videos based on reference images.
Native vertical outputs for Ingredients to Video (portrait mode) to power mobile-first, short-form video creation.
State-of-the-art upscaling to 1080p and 4K resolution 1 for high-fidelity production workflows.

These updates are launching in the Gemini app, YouTube, Flow, Google Vids, the Gemini API and Vertex AI.

4. Vibe querying with comments-to-SQL: Crafting complex SQL queries can be challenging. Often, engineers simply want to express their data needs in plain English directly within their SQL workflow. That’s why we’re introducing Comments to SQL in BigQuery. This feature makes writing queries using natural language – ‘vibe querying’ – a reality. Learn more in the blog.

News you can use

Mastering Gemini CLI: Your complete guide from installation to advanced use-cases: We’ve teamed up with DeepLearning.ai and are excited to announce a free course – Gemini CLI: Code & Create with an Open-Source Agent. This course isn’t just for developers; we dive into practical use cases for various tasks such as data analysis, content creation, and personalized learning.
How Google SREs use Gemini CLI to solve real-world outages: In this article, we’ll delve into real scenarios that Google SREs are solving today using Gemini 3 (our latest foundation model) and Gemini CLI—the go-to tool for bringing agentic capabilities to the terminal.
Getting started with Gemini 3: Deploy your first Gemini 3 app to Google Cloud Run: In this blog, we will show you how to vibe code your first app—which leverages the Gemini 3 Flash Preview model and deploy it as a publicly accessible URL on Google Cloud Run. Google AI Studio lets you go from idea to app quickly by using natural language to generate fully functional apps using the power of Gemini 3.
Practical guidance: Building with the Secure AI Framework (SAIF) on Google Cloud: We know that security and data privacy are the top concern for executives when evaluating AI providers, and security is the top use case for AI agents in a majority of industries. To help you build AI boldly and responsibly, here’s our guide to developing AI with the Secure AI Framework (SAIF) on Google Cloud.
The truths about AI hacking that every CISO needs to know (Q&A): How will AI boost threat actors? And what can chief information security officers do about it? Google’s Heather Adkins, vice-president, Security Engineering, explores how securing the enterprise is about to change.

Cloud CISO Perspectives: At Next ‘26, why we’re multicloud and multi-AI

Thu, 30 Apr 2026 16:00:00 +0000

Welcome to the second Cloud CISO Perspectives for April 2026. Today, Francis deSouza, COO Google Cloud and President, Security Products, explains why Google is multicloud and multi-AI, straight from Next ‘26.

As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the Google Cloud blog. If you’re reading this on the website and you’d like to receive the email version, you can subscribe here.

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Cybersecurity in the era of the agentic enterprise

By Francis deSouza, COO Google Cloud and President, Security Products

Francis deSouza, COO Google Cloud and President, Security Products

Last week at Google Cloud Next ‘26, we announced 220 products, and signaled a paradigm shift. We are not just moving workloads to the cloud; we are entering the era of the agentic enterprise.

The AI megatrend, coupled with an accelerating cloud adoption, is the most profound enterprise IT transformation of our lifetimes. It is igniting a new wave of innovation, and also demands a fundamental re-architecting of cybersecurity. Our vision at Google Cloud is clear: to be the most AI-native, open, and secure platform on the planet, meeting enterprises exactly where they are.

Security at machine speed: From minutes to seconds

In this new landscape, IT resilience is defined by a multi-AI and multicloud strategy. A durable AI roadmap cannot rely on a single model or a single cloud provider. For CISOs, the mission-critical frontlines have shifted to securing models, agents, and the data that fuels them.

AI isn't just a security challenge — it is also the ultimate security tool. Today, our security operations center (SOC) agents automatically triage tens of thousands of unstructured threat reports every month. The results of our AI-first cyberdefense are transformative:

90% reduction in threat mitigation time by filtering noise and extracting intelligence instantly.
30 minutes to 60 seconds: Our Triage and Investigation agent, powered by Gemini, has processed over 5 million alerts this year, turning half-hour manual tasks into one-minute automated actions.
98% accuracy: Our new dark web intelligence capability analyzes millions of daily external events to surface the threats that actually matter.

The multicloud reality is non-negotiable

Modern organizations are multicloud by default. Between hyperscalers, SaaS vendors, and legacy systems, the single cloud dream is over. Our ethos has always been open because that is the only way to protect a fragmented world.

The reality is that AI and cloud applications are built across multiple platforms and models. To protect them, we focus on making it easier and faster to mitigate risk across all major cloud environments.

By unifying security across all major cloud environments, we aren't just simplifying management — we are lowering the stakes. Our unified approach reduces the risk and cost of a breach by 70%.

The integration of Wiz into Google Cloud has further deepened this advantage. With 90% of environments now running self-hosted AI software, Wiz allows us to secure the entire AI development lifecycle across any cloud, complementing our deep expertise in threat intelligence.

The Google advantage: From lab to live on day 1

The speed of innovation in AI is relentless. Standard security industry timelines of six months to a year to incorporate the latest models into security products are not sufficient; they leave organizations two generations behind their adversaries.

Francis deSouza, COO Google Cloud and President, Security Products, explains Google Cloud's multicloud and multi-AI approach to Next '26 attendees in Las Vegas.

Google occupies a unique position in this race. We co-design the entire stack: hardware, AI, and security.

Vertical integration: We are the only security provider that integrates a new model on day 1.
Research to reality: When Google DeepMind achieves a breakthrough in the lab, we move it to your security platform faster than anyone else in the industry.

A blueprint for the agentic future

As we advocate for a multi-AI world, we are providing the tools to build it safely. Our latest whitepaper, Building Secure Multi-Agent Systems on Google Cloud, is a robust framework for this transition.

It highlights the power of our newly announced Gemini Enterprise Agent Platform, featuring:

Agent Gateway: A single governance layer for identity and access management.
Model Armor: Sophisticated prompt sanitization to prevent adversarial attacks.
Agent Identity: Ensuring that as agents move at machine speed, they do so with authenticated authority.

The announcements at Next ‘26 were more than a recap; they were a promise. We are committed to being your partner in this new era — providing the most open, productive, and secure foundation for the AI-driven future.

You can also catch up on all our Next ‘26 security announcements here.

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In case you missed it

Here are the latest updates, products, services, and resources from our security teams so far this month:

Next ‘26: Redefining security for the AI era with Google Cloud and Wiz: At Google Cloud Next, we showcased how we can help you defend against threats at machine speed, protect AI and multicloud environments, and secure cloud workloads at scale. Read more.
Next ‘26: Introducing Google Cloud Fraud Defense, the next evolution of reCAPTCHA: We’ve launched Google Cloud Fraud Defense, the trust platform for the agentic web and the next evolution of reCAPTCHA. Read more.
Next ‘26: New partner-supported workflows for Google Security Operations: We’ve introduced new partners for Google Security Operations as part of the Google Cloud Security Integration Ecosystem program. Read more.
How Google Does It: An inside look at cybersecurity: Learn how Google approaches some of today's most pressing security topics, challenges and concerns, straight from Google experts. View the collection.
The current state of prompt injections on the web: Our threat intelligence teams initiated a broad sweep of the public web to monitor for known indirect prompt injection patterns. This is what we found. Read more.

Please visit the Google Cloud blog for more security stories published this month.

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Threat Intelligence news

Defending your enterprise when AI models can find vulnerabilities faster than ever: Now is the time to strengthen playbooks, reduce exposure, and incorporate AI into security programs. Here’s an overview of the evolving attack lifecycle, how threat actors will weaponize these capabilities, and a roadmap for modernizing enterprise defensive strategies. Read more.
German cyber criminal Überfall and shifts in Europe's data leak landscape: Germany has reclaimed its position as a primary focus for cyber extortion in Europe. While data leak site posts rose almost 50% globally in 2025, Google Threat Intelligence (GTI) data shows that the surge is hitting German infrastructure harder and faster than its regional neighbors. Read more.
How UNC6692 employed social engineering to deploy a custom malware suite: Google Threat Intelligence Group (GTIG) has identified a multistage intrusion campaign by a newly-tracked threat group, UNC6692, that used persistent social engineering, a custom modular malware suite, and deft pivoting inside the victim’s environment to achieve deep network penetration. Read more.

Please visit the Google Cloud blog for more threat intelligence stories published this month.

Now hear this: Podcasts from Google Cloud

AI, Zero Trust, and secure by design walk into a bar: Is there Zero Trust for AI? Why is secure by design picking up speed now, just as issues of machine identity come to the fore? Grant Dasher, distinguished engineer, Google, analyzes the intersection of trust, secure design, and AI with hosts Anton Chuvakin and Tim Peacock. Listen here.
From CISA to cloud: AI assurance, concentration risk, and the new regulatory frontier: Jeanette Manfra, VP, head of Risk and Compliance, Google Cloud, joins Anton and Tim to discuss the current regulatory landscape facing cloud and AI, and the ongoing tug-of-war between security and privacy at the enterprise level. Listen here.
More than just packets: Is NDR a first-class cloud security control: Extrahop’s Raja Mukerji and Rafal Los join Anton and Tim to delve into the value proposition of network detection and response in 2026, and how it can apply to the worlds of work from home, cloud and SaaS, encryption, and high bandwidth. Listen here.
Defender’s Advantage: Takeaways from the 2026 M-Trends report: Host Luke McNamara is joined by Mandiant’s Chris Linklater to discuss the breach trends throughout 2025 and into this year. He notes key areas that organizations should focus on as we approach the mid-point of 2026. Listen here.
Cyber-Savvy Boardroom: Head in, hands out: Mark Lobel, formerly of PwC, joins hosts Alicja Cade and David Homovich to discuss why high-stakes simulations are essential to protecting corporate reputation when the regulatory clock is ticking. Listen here.

To have our Cloud CISO Perspectives post delivered twice a month to your inbox, sign up for our newsletter. We’ll be back in a few weeks with more security-related updates from Google Cloud.

The founder’s AI foundation: The top announcements for startups from Next ‘26

Wed, 29 Apr 2026 16:00:00 +0000

The momentum is undeniable: the world’s fastest-growing AI startups are building with Google Cloud. Instead of stitching together fragmented point solutions, founders are building their businesses here because we offer the entire AI stack in a single, open environment.

And, as we saw at Next ‘26 last week, we continue to advance the models, infrastructure, platforms, security, and governance that allow startups to build faster and dream bigger.

You can already see this scale and velocity in action every day. Platforms like Lovable are empowering developers to generate over 200,000 new projects daily. Thinking Machines Labs is leveraging the latest NVIDIA Blackwell chips through Google Cloud to double its training and serving speeds. When it comes to activating complex data, Parallel bypassed the multi-vendor headache, using our integrated ecosystem — bridging Gemini, BigQuery, and Spanner — to massively scale their high-accuracy search APIs.

Startups are moving at this pace because we’ve eliminated the operational plumbing. Aible is running complex enterprise agents directly where their data lives in BigQuery, while teams like Emergent AI are using Google Kubernetes Engine (GKE) to autonomously scale thousands of secure sandboxes for vibe coding natural language prompts into production-ready applications.

By putting everything in one place, we provide the ultimate foundation so you can focus 100% on shipping the AI-native products your customers are waiting for.

Let’s look at some of the biggest announcements out of Next ‘26 and what they mean for you.

1. Gemini Enterprise: comprehensive agent platform

The new Gemini Enterprise Agent Platform is how we’re moving beyond isolated AI tools to a complete lifecycle platform. It evolves the model building capabilities of Vertex AI with advanced features for:

Building: The upgraded Agent Development Kit (ADK) introduces a graph-based framework for complex multi-agent reasoning, while the low-code Agent Studio allows you to move seamlessly from prompts to deployed agents. Developers can also utilize Agent Garden to jumpstart development with pre-built agent templates.
Scaling: The re-engineered Agent Runtime delivers sub-second cold starts and supports long-running agents that maintain state for days at a time. The new Agent Memory Bank enables agents to recall high-accuracy details for personalized, long-term context.
Governing: To ensure security and compliance, the platform introduces Agent Identity for trackable auditing, Agent Gateway for unified connectivity and policy enforcement, and Agent Threat Detection to flag suspicious behavior in real time.
Optimizing: Teams can ensure quality using Agent Simulation to test against synthetic interactions, and rely on Agent Optimizer to automatically analyze real-world failures and suggest improvements.

What it means for startups: Startups no longer need to stitch together fragmented tools to build complex AI systems. The new Agent Platform provides every tool in one place — whether you are using the visual interface of Agent Studio to quickly prototype or the code-first logic of the ADK for advanced orchestration, a lean team can build and scale production-ready agents with security guardrails from day one. This approach helps eliminate technical debt and drastically accelerates time-to-market.

2. The Agentic Data Cloud: Data that actually does the work

We are moving your data from rows and columns to autonomous action. This is where you build AI agents that understand your business logic and execute the next best action—closing the gap between simply analyzing data and actually resolving real tasks on your behalf. Because agents generate orders of magnitude more workloads, our Agentic Infrastructure scales to handle bursty startup demands, improving query speeds and reducing infrastructure costs.

Query anywhere, leave data where it lives: The Cross-Cloud Lakehouse lets you use Google's advanced AI on data living in AWS or Azure. Build smarter agents without requiring migrations, experiencing vendor lock-in, or incurring additional costs to move your data.
The zero-ETL startup: Features like One-Click Reverse ETL and Lakehouse Federation collapse the walls between operational databases and analytics, allowing data to flow seamlessly without manual engineering.
Give your agents business context: Before an agent acts, it needs to understand your specific business rules. The new Knowledge Catalog turns passive data into active semantics, ensuring your agents know the exact difference between "booked revenue" and "projected revenue" without guessing or hallucinating.
Eliminate "glue code" and manual connectors: Fully managed, remote Model Context Protocol (MCP) servers are now embedded directly across the database portfolio (Firestore, Spanner, BigQuery, and AlloyDB).
Production-grade guardrails: Connecting AI to your database usually means risking dangerous SQL hallucinations. New Tools for Data Agents solve this by providing pre-built, modular building blocks that deliver near-100% text-to-SQL accuracy, letting your custom agents interact with live data safely and reliably.
Instant app generation: A new integration with Google AI Studio allows developers to use natural language for vibe coding to turn a simple text prompt into a live, deployed application connected to Firestore in seconds.

What it means for startups: Your proprietary data can be a competitive moat, but activating it usually requires a massive integration tax — breaking down cloud silos, writing brittle glue code, and managing complex ETL pipelines. Our latest data advances eliminate the friction. Startups can now leave their data right where it lives in AWS or Azure, "vibe code" an app into existence, and instantly plug agents into secure databases using standard MCPs. It turns data integration from a multi-week engineering sprint into a single afternoon's work.

3. Next-Gen Compute: Performance and Cost-Efficiency

Google Cloud is drastically expanding its AI Hypercomputer portfolio to offer increased performance-per-dollar, while introducing autonomous features that handle the heavy lifting of infrastructure management for lean teams:

TPU 8t for training: Optimized for training massive models, the TPU 8t achieves nearly 3x higher compute performance than the previous generation and delivers up to 2x better performance-per-watt.
TPU 8i for inference: Designed to make running agents more affordable, the TPU 8i delivers 80% better performance-per-dollar for inference than the prior generation.
Axion N4A for general compute: Google Cloud Axion instances deliver 100% better price-performance than comparable x86 instances, ensuring highly efficient, sustained operations.
Multi-agent networking: New C4N and M4N machine series offer a nearly 4x increase in network bandwidth, purposefully built to handle high-volume communication between AI agents.
GKE sub-second agent scaling: Google Kubernetes Engine Agent Sandbox now deploys 300 sandboxes per second, per cluster. This delivers sub-second cold starts for AI workloads, ensuring instant response times and secure multi-tenant isolation.
The self-driving cloud: Google Cloud integrated Gemini reasoning directly into its telemetry. The system now performs autonomous root-cause analysis, identifying and fixing infrastructure misconfigurations before a human even realizes there’s a problem.
NVIDIA integration: Google remains at the bleeding edge of partner hardware, becoming one of the first to offer the NVIDIA Vera Rubin NVL72 alongside Blackwell and Hopper.

What it means for startups: Managing that infrastructure can drain precious engineering cycles. Google Cloud’s expanded portfolio solves both problems. You can train models faster with the TPU 8t, scale millions of concurrent agents cost-effectively using the TPU 8i, and deliver zero-latency experiences using GKE sandboxes.

More importantly, with our autonomous root-cause analysis handling the operational plumbing, your engineers are freed up to focus on what actually matters: building your product.

4. Agentic Defense: Security as Your Enterprise Go-To-Market Engine

Google Cloud is combining its global threat intelligence with Wiz's Cloud and AI Security Platform to provide startups with a fully automated, unified security posture from code to cloud:

API economics protection: The new Google Cloud Fraud Defense acts as an intelligent bouncer, preventing malicious bots from scraping IP or running up massive compute bills on unauthorized agent interactions.
Full-stack AI protection: The Wiz AI Application Protection Platform (AI-APP) secures every layer of the AI stack and natively supports the "outer layer" of the cloud, now securing tools startups actively use like Vercel, Databricks, and Cloudflare.
Google-scale threat intelligence: New agentic SecOps tools, like the Threat Hunting Agent and dark web intelligence, automate detection rules and elevate only the threats that matter with 98% accuracy. Meanwhile, the Triage and Investigation agent uses Gemini to shrink 30-minute security investigations down to just 60 seconds.
SecOps agents: We introduced three new agents in Google Security Operations — Threat Hunting (proactive pursuit of stealthy threats), Detection Engineering (automated detection creation), and Third-Party Context (workflow data enrichment) — that empower teams to defend at the speed of AI.
Drag-and-drop security automation: Wiz Workflows introduces a new hub with a customizable drag-and-drop interface, allowing lean teams to easily orchestrate how and when these AI agents act without writing complex security scripts.

What it means for startups For B2B startups, the enterprise information-security review is where deals often go to die. By building on our unified security foundation, you can demonstrate that your application has continuous, automated red-teaming and runtime protection.

It turns the dreaded vendor risk assessment into a competitive advantage, allowing you to bypass procurement roadblocks and close enterprise deals faster without needing to hire a massive SecOps team.

5. The Go-To-Market Engine & Ecosystem Capital

Google is solving the ultimate startup hurdles — distribution and funding — by opening up its enterprise channels and deploying massive capital to partner ecosystems:

Agent Gallery in Gemini Enterprise: Startups can now monetize their customized agents directly inside the Gemini Enterprise app via Google Cloud Marketplace. This reaches millions of users right in their daily workflows and turns everyday user discovery into an automated procurement flow for IT, accelerating purchasing cycles by up to 50%.
$750-million fund: A capital injection dedicated to partner agent development and co-marketing, designed to fuel the next generation of AI builders.

What it means for startups Building the product is only half the battle; surviving enterprise procurement is the other. By integrating into the Agent Gallery, your startup can reach millions of Gemini Enterprise users directly in their daily workflow, where they can easily click to request IT procurement. Plus, their purchases can draw down on existing Google Cloud commitment, allowing you to tap into a $240 billion backlog. Paired with the $750-million partner innovation fund, Google is putting capital and distribution directly behind your growth.

Start building with the AI Agents Challenge

We’ve given you the architecture, the security, and the distribution channels — now it’s time to get hands-on. To help you accelerate your development, we are launching the Google for Startups AI Agents Challenge. Open globally to eligible startup founders and developers, this six-week competition equips your team with $500 in cloud credits and access to our latest AI tools, including Gemini Enterprise, so you can build autonomous systems and compete for a share of a $90,000 prize pool.

We’re offering separate tracks, whether you want to build a net-new agent from scratch, optimize an existing prototype for production, or prep a business-ready agent for enterprise distribution, there is a track tailored to your exact stage. Submissions are open until June 5, 2026, and will be evaluated on technical implementation, business case, innovation, and your final demo. Learn more and sign up for the challenge here.

UKG unlocks real-time workforce intelligence at scale with the Agentic Data Cloud

Wed, 29 Apr 2026 16:00:00 +0000

At UKG, we’ve spent years building and expanding our human capital management (HCM) and workforce management (WFM) solutions with new products, capabilities, and a series of acquisitions. Our cloud platform includes a suite of connected systems that support every corner of the employee experience, including scheduling and workforce operations, HR and payroll, and culture and engagement tools.

These connected tools offer customers incredible depth, but it also means our backend reflects years of evolution. We have 126 application teams, dozens of tech stacks, and more than 12,000 database instances inherited through acquisitions and product growth. And each product carries its own schema and operational footprint.

Previously, data moved through bespoke pipelines not built for real-time use. As AI advanced, expectations did too. Customers wanted instant insights across HR, time, pay, culture, and operations, and those insights increasingly needed to drive automated workflows and intelligent applications.

Internally, teams needed consistent, high-performance access to shared data to innovate faster and modernize our architecture. We needed a unified foundation for the next generation of intelligence across our suite. That’s why we built People Fabric, our new data and intelligence platform powered by AlloyDB for PostgreSQL and the just-announced Agentic Data Cloud.

Unifying the systems behind the suite

People Fabric started with a simple need: bring the full UKG suite onto one real-time foundation. Getting there started with defining a single canonical data model for the entire suite. This would serve as the shared language for people, work, pay, and culture data — consistent no matter where the information originated.

We needed an operational database that could ingest changes quickly and scale horizontally. That’s why we chose AlloyDB as the core of People Fabric. It gives us millisecond-level read-after-write behavior, high-throughput ingestion, scalable read pools, and native vector capabilities to support AI.

With the model defined and the operational store selected, the next step was building the pipeline that feeds the platform. We created a custom change data capture (CDC) framework to extract changes from our existing operational databases inherited over the years. Those changes flow through Dataflow, where they’re transformed into the canonical structure that AlloyDB for PostgreSQL expects.

Once in AlloyDB, that data becomes the real-time backbone of the platform. Applications use it for near-instant queries. AI agents rely on it for cross-domain decisions, and vector search engines use it to power natural-language and similarity-based experience layers.

For larger analytical workloads, the same data flows into BigQuery, which gives our teams and our customers the ability to perform organization-wide reporting and analysis without straining the system. Cloud SQL holds the metadata and tenancy context that govern who can see what and how different parts of the suite interact with People Fabric. From there, the system runs continuously. Data enters through streaming ingestion and gets modeled once in AlloyDB for PostgreSQL to make it available everywhere.

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Bringing people intelligence to intelligent people

With the architecture in place, People Fabric gives us something we never had before: a complete and consistent view of people, work, pay, and culture data that’s updated continuously and ready for AI to use in real time.

That unified context is what powers our assistive experiences, including conversational reporting and natural-language interactions. Leaders can ask questions in plain English and get answers that reflect the full picture — not just a single system’s slice of it.

With the power of Google’s Agentic Data Cloud, our platform unifies analytical and transactional data to power real-time AI. This allows agents to reason over live workforce signals and trigger immediate actions. Because this data is governed and modeled from the start, our agents can reliably handle multi-step workflows across HR, payroll, and timekeeping.

Whether they're identifying pay discrepancies, adjusting schedules, or flagging compliance risks, they operate with the same shared semantics and security model that guides our applications. It’s the difference between AI that reacts and AI that can truly assist.

Driving impact across every layer

For engineering teams, People Fabric acts as a database-as-a-service that removes the need for each microservice to manage its own datastore or pipelines. This accelerates development and supports modernization without customer disruption.

AlloyDB for PostgreSQL delivers millisecond read-after-write behavior, zero replication lag, and near-real time ingestion latency, enabling real-time workloads with far less complexity. Migrating core person and employment data off our on-prem monolith has generated cost savings significant enough to fund half of People Fabric.

Real-time operational data now gives managers a live view of staffing, pay, and workforce activity. More than 1,000 organizations are already on the platform, with another 1,000 in progress.

As we continue expanding People Fabric, we’re laying the groundwork for deeper agentic automation, more responsive analytics, and a growing set of AI-driven capabilities — all on a trusted, scalable foundation built for what’s next.

Learn more

UKG’s success illustrates how leveraging AlloyDB for PostgreSQL and the Agentic Data Cloud allows organizations to unify operational and analytical data, creating the essential foundation for real-time, agentic AI.

Learn more about AlloyDB for PostgreSQL and get started with a free trial today!
Sign up for a strategy workshop today on how to get your data ready for the agentic era!

50+ fully managed MCP servers now available for Google Cloud services

Tue, 28 Apr 2026 17:00:00 +0000

At Google Cloud Next ‘26, we announced that more than 50 Google-managed Model Context Protocol (MCP) servers are generally available or in preview, with more on the way.

Why it matters: To move beyond experimental prototypes, AI agents must be able to access real-world data and solve complex problems autonomously.

Google-managed Managed Context Protocol (MCP) servers provide the critical connectivity to bridge AI agents with the vast Google and Google Cloud ecosystems. By hosting these servers on an enterprise-ready, standardized platform, we eliminate the need to integrate with local MCP servers and offer a unified developer experience that’s integrated across major agent runtimes and frameworks.

MCP built for the enterprise

Scaling your agent ecosystem shouldn't be a trade-off between speed and safety. You need the flexibility to grow, but you also need the guardrails to manage and govern your agents.

By pointing your AI agents toward Google-managed MCP endpoints, you’re plugging into the Google Cloud security stack, without needing to make regional configuration changes . While the platform offers deep flexibility for a range of architectures, here’s a snapshot of their capabilities that make it easier:

Strong interoperability: Through protocol translation, your agents stay compliant with the MCP specification, so that they play nice with public agents and agent frameworks like Gemini CLI, Claude, ChatGPT, VS Code, LangChain, Agent Development Kit (ADK), and CrewAI right out of the box. For example, we added support for Resources and Prompts as primitives in the MCP protocol in addition to Tools.
Centralized discovery: No more hunting for MCP servers and tools. Agent Registry offers a unified directory where you can easily find and manage agents, MCP servers, and tools in one place.
Easy access with security and governance: Every Google Cloud service is now MCP-enabled by default, allowing agents to easily communicate with Google Cloud. Leverage native Cloud IAM Deny policy for fine-grained access control.
Content safety: With Model Armor in-line integration, you can actively defend against indirect prompt injections and data exfiltration.
Full observability: Maintain full oversight with OTel Tracing and Cloud Audit Logs, helping you troubleshoot your agents, get comprehensive analytics, and perform forensic audits of agentic actions.

Case study: Insta360 redefines video editing

Insta360, one of the world’s leading smart imaging brands, is leveraging the Google Cloud agentic ecosystem to redefine how users capture and share their lives. Building on its "Moments" feature, which delivers AI video highlights, the company developed an AI video editing agent using Google's Agent Development Kit, Agent Engine, A2A and Google-managed MCP servers, allowing users to complete video editing in the cloud through natural language input.

“Transitioning to managed MCP servers will allow us to move away from fragile point-to-point connections and toward a secure, scalable service-oriented architecture. By exposing our proprietary editing tools as managed endpoints, we're gaining the enterprise-grade stability needed to bring autonomous video creation to users around the world.” - Even Lin, Head of Cloud Services, Insta360

Broad coverage across the Google ecosystem

Whether you’re automating internal operations or building customer-facing experiences, Google-managed MCP servers let your models do more than just chat; they provide the secure connections needed to take direct action across your Google Cloud services. Here are a few ways you can leverage them today:

1. Infrastructure, operations, and security

Agents can help move beyond simple monitoring to active orchestration, handling maintenance and monitoring while prioritizing critical security events. Here are a few examples of what this looks like in practice:

Automated lifecycle management (ALM): Provision and decommission resources dynamically based on real-time application demand using GKE, Cloud Run, or GCE MCP servers.
Self-healing systems: Empower agents to monitor events via Cloud Logging and Monitoring, triggering recovery actions like traffic rerouting or deployment rollbacks before users are impacted.
Security orchestration: Build sophisticated workflows that use Google Security Operations to automatically investigate and respond to emerging threats.
Fleet and network ops: Use the Android Management API MCP server to query device health in natural language, or utilize Network Management API MCP server to automate complex diagnostic workflows and surface actionable insights instantly.

2. Databases, analytics, and storage

To be effective, an agent must be grounded in enterprise truth — the live, operational data residing in your production systems. This enables agents to interact with your data ecosystem with:

Real-time operational insights: Connect agents directly to Spanner, AlloyDB, Cloud SQL, Firestore, or Bigtable MCP servers to give agents access to both structured and unstructured operational data, to power immediate, data-driven decision-making.
Analytical insights and data pipelines: Leverage BigQuery and Managed Service for Apache Spark MCP servers to process large datasets, using Pub/Sub or Managed Service for Apache Kafka to trigger proactive system alerts.
Contextual retrieval: Access both structured and unstructured data with Google Cloud Storage and Knowledge Catalog MCP servers to provide agents with real-time context required to accurately execute complex tasks.

3. Services and apps

Beyond raw data, agents require geographic context, technical documentation, and productivity tools to operate effectively. Here are ways agents can leverage these tools to perform more sophisticated and helpful tasks:

Developer assistance: The Developer Knowledge API MCP server grounds AI agents in Google's official developer documentation, allowing tools to reference up-to-date code samples and guides to solve complex technical issues as they happen.
Hyper-local contextual intelligence: Use Maps Grounding Lite MCP server to provide agents with trusted Google Maps data. Deliver high-precision responses for routing, weather, and local points of interest, minimizing hallucinations in travel and real-estate applications.
Conversational experience design: The Gemini Enterprise Agent Platform MCP server empowers agents to act as AI supervisors, programmatically managing the full lifecycle of other models, prompts, and endpoints. Additionally, the Customer Experience Agent Studio MCP server enables AI-assisted workflows for building, modifying, and maintaining customer experience agents.
Productivity and commerce: Streamline team collaboration with Workspace MCP Servers for Gmail, Drive, Calendar, People API, and Chat by allowing agents to summarize Gmail threads, draft Docs, manage Calendar invites and facilitate Google Chat workflows to streamline team productivity. Additionally, with Google Pay and Wallet MCP server, you can integrate payments and digital passes into your agentic workflows, allowing AI to assist with onboarding, troubleshooting integrations, and monitoring checkout performance.

By unifying these capabilities with MCP, it’s easier than ever to build agents that don't just chat, but actually take actions on your behalf.

Technical scenario with demo

To showcase the power of this platform, we built the Pet Passport demo using Google-managed MCP servers and ADK.

This demo features an autonomous agent that handles a complete end-to-end workflow: planning the perfect pet-friendly day out in New York City. The agent implements a macro-to-micro reasoning chain:

First, it uses the BigQuery MCP server to analyze the NYC Dog License dataset and identify the specific neighborhood where a user's dog breed is most popular.
Next, it passes that context to the Google Maps MCP server to generate a verified walking route complete with dog parks and cafes.

When it's time to go live, Gemini CLI leverages the Cloud Run MCP server to deploy the agent application directly from a local machine, publishing the entire experience as a live, shareable URL.

Try it yourself with the codelab or explore the source code on GitHub.

Start building today

We invite you to build production-grade workflows using the Gemini CLI, ADK, or your preferred IDE.

Day 2 at Google Cloud Next: A marathon developer keynote

Thu, 23 Apr 2026 13:00:00 +0000

At Google Cloud, every day is Developer Day, but none so much as day 2 of Google Cloud Next, when we hold the developer keynote. This year’s topic? An in-depth look at Gemini Enterprise Agent Platform. This year’s theme? Planning a marathon for 10,000 participants through the Las Vegas Strip.

OK, let’s run with it.

Gemini Enterprise Agent Platform: A warm up

As the evolution of Vertex AI, Agent Platform “allows you to build autonomous agents that proactively help users — and complete tasks independently,” said Brad Calder, President, GCP and SRE. The platform does so with a whole suite of tools and capabilities to build, scale, govern, and optimize your agents.

Brad then passed the baton to keynote emcees Richard Seroter, Chief Evangelist, and Emma Twersky, Developer Relations Engineer, for an in-depth run-through of the agentic marathon simulator.

The system uses three main agents: A planner to determine routes; an evaluator that assesses routes based on business and community requirements; and a simulator that takes the route, adding actors and randomized behaviors to test the impact on the city,

This, Emma said, turns out to be “a great example of how agents can help us plan, simulate, and think about solving a really big challenge.”

First off the blocks: Building the agent

Mofi Rahman, Developer Relations Engineer, came onstage to demo how the Agent Development Kit (ADK), Google Cloud remote Model Context Protocol (MCP) servers, and Agent Runtime provide the planner agent with the Instructions, Skills, and Tools it needs to improve the initial agent. By the end of the demo, the simulator had generated a route.

“The simulated route looks beautiful,” said Mofi. “Looks like the runners are going to get an amazing view of the entire Las Vegas Strip.”

An agent to evaluate the agent

Next, Ivan Nardini, Developer Relations Engineer, and Casey West, Architecture Advocate, showed us how to evaluate the agent, and build a UI for it. “We want to show you how to move from fragile, unpredictable agentic loops, to a rigorously evaluated network of experts that literally build their own UI,” Casey said.

They did so by deploying a separate model to judge the route, checking both deterministic (e.g., route length) and non-deterministic (e.g., community impact) criteria. For UI development, they showed off the Agent-to-User Interface, or A2UI, an open-source standard developed by Google that created an interface in a single shot. They also used the Agent-to-Agent Protocol, or A2A, and Agent Platform’s Agent Registry, to connect and see which agents are deployed.

“Think of Agent Registry as the DNS of your internet of agents,” Casey said.

Agents that never forget

Richard then mused about how to build agents that get better with time, that “take the learnings from the simulation and optimize for the next run.” Because the answer shouldn’t be to “cram raw text in every request we send back to our agents.”

To capture this learned knowledge, Agent Platform offers Agent Platform Sessions and Memory Bank, plus the ability to turn to tools like Spark or a database to retrieve more information, resulting in an even stronger simulator.

When agents go off course

Thus far, everything had gone swimmingly, but then Richard accidentally “broke” the simulator agent. That provided a perfect opportunity for Megan O’Keefe, Senior Staff Developer Advocate, to show off Agent Interoperability and Gemini Cloud Assist, and how to use them to debug agents at scale.

“With these autonomous agents, the production challenge isn’t just scaling the infrastructure, it’s managing the reasoning, the tool calls — all the places in the system where something can go wrong!” Megan said.

Megan used Agent Runtime trace view to see where the problem was, and using natural language, launched a Cloud Assist Investigation to explore logs and events, which pointed to a specific line of code as the offender. Megan then opened up her Antigravity IDE (powered by Gemini 3, and connected via MCP) to find the problem (an insufficiently run “event compaction” run) and to suggest a fix (add a token_threshold parameter to the event compaction config). She approved the fix and committed it to source, triggering a redeployment to Agent Platform. Problem solved!

Scaling the agents

To this point, all of the presenters had been showing off agent services running as Cloud Run services. Bobby Allen, Group Product Manager, then showed how to convert the apps to Google Kubernetes Engine (GKE), which provides greater control, as well as to use a customized Gemma 4 model, all by vibe coding in the Antigravity editor, which is connected to Cloud Assist. Along the way, Bobby also migrated the agents from GCSFuse to a high-performance Lustre file system.

Closely related to scaling is sharing — making agents available for the world to use and build on. Ines Envid, Senior Director, Product Management and Jason Davenport, Area Technical Lead, showed how to build no-code agents from the Gemini Enterprise app, and how to integrate them with other, “high-code” agents.

Shifting down

Last but not least, it was time to talk about security and governance. “Agents give users and other agents new ways to intentionally — or unintentionally — expose data and behavior in ways that we may not want,” mused Emma.

The standard response to that is to “shift left” — move testing, quality, and performance evaluation earlier in the development process — but for developers, that usually means more work, Richard said. “It’s not sustainable for developers to be responsible for all the layers of the stack,” he said. Instead, “we need to shift down.”

To help, there’s Agent Identity and Agent Gateway, demoed by Ankur Kotwal, head of Cloud Developer Relations. Ankur showed how Agent Gateway uses IAM policies to ensure agent actions are only accessible by approved sources, and how Agent Identity provides each agent with a unique and immutable credential. Then, Agent Policies can be configured to provide guardrails for the agents.

Yinon Costica, Co-Founder and VP of Product at Wiz, then went a step further and showed how Wiz can scan your agent code and infrastructure, and Wiz Green Agent can suggest root cause remediations.

“It’s a full architecture for security to easily understand what you built without you having to actually explain it,” Yinon said. Better yet, he also showed using this functionality from Anthropic’s Claude Code with Opus. “With Wiz, we want to enable your choice of tools and models to fix and prevent real risks,” he said.

The finish line

With this, the developer keynote came to an end. But for Google Cloud developers, it’s just the beginning, as the entire solution is available as source code in GitHub, and all the demos are available as Codelabs. Because when it comes to agentic development, these resources will really help you hit the ground running.

Day 1 at Google Cloud Next ‘26 recap

Wed, 22 Apr 2026 23:00:00 +0000

Last year at Google Cloud Next ‘25, we asked you to imagine a new future for AI. At Next ‘26, the question before you is how do you move AI into production across your entire enterprise?

According to Google Cloud CEO Thomas Kurian, the answer is straightforward: You need a unified stack, with “chips that are designed for models, models that are grounded in your data, agents and applications that are built with those models,” and the whole thing “secured by the infrastructure,” Thomas said in his keynote. (This is the same unified stack that Google uses for Search, YouTube, Chrome, and Android. As Alphabet CEO Sundar Pichai said in his opening remarks, “a big focus of ours is to always be customer zero for our own technologies.”)

As AI matures, we’ve laid out a blueprint on how to succeed. Read on for a whirlwind tour of what we announced from the keynote stage.

Gemini Enterprise: The end-to-end system for the agentic era

Throughout this unified stack is Gemini Enterprise — “the connective tissue between your data, your people, and your goals,” Thomas said, providing a combination of intelligence and automation across multiple layers. Here’s what’s new.

1. Gemini Enterprise Agent Platform

Gemini Enterprise Agent Platform is where you go to build, scale, govern, and optimize agents. As the evolution of Vertex AI, it’s built on top of our leading infrastructure, and deeply integrated with our data and security capabilities — the foundation of the Agentic Enterprise. Here’s a sampling of Agent Platform’s new features and capabilities:

Build: Choose the right environment for the job — from the low-code, visual interface of the new Agent Studio, to the code-first logic of the upgraded Agent Development Kit (ADK). We’ve simplified the entire lifecycle with AI-native coding capabilities to help you ship production-grade agents faster.
Scale: Clear the path to production with the re-engineered Agent Runtime. This supports long-running agents that maintain state for days at a time and are backed by Memory Bank for persistent, long-term context.
Govern: Establish centralized control with Agent Identity, Agent Registry, and Agent Gateway. These capabilities help ensure every agent — whether built on Agent Platform or sourced from our partner ecosystem — has a trackable identity and operates within enterprise-grade guardrails.
Optimize: Guarantee quality with Agent Simulation, Agent Evaluation, and Agent Observability. These tools provide full execution traces and a real-time lens into agent reasoning to help ensure your agents always hit their goals.

To dive deep into Agent Platform, read more in our announcement blog here.

2. Gemini Enterprise app

The Gemini Enterprise app is “the primary environment where your business actually operates,” Thomas explained. The app is where many workers, especially non-technical ones, can ask questions of enterprise agents, create generative media, engage with prebuilt agents, and even create their own with conversational interfaces — all with governance, compliance, and security built in. Here’s a sample of what’s new in this foundational interface:

Gemini Enterprise Projects give your agents permanent memory.
Deep Think solves your most complex business challenges without context pollution.
Microsoft 365 interoperability makes it easy to export docs you create with Canvas into Microsoft Office formats.

To illustrate the power of Gemini Enterprise, Shaun White, three-time Olympic gold medalist, entrepreneur, and snowboarding legend, joined us on stage.

“Back when I was training, our tools were camcorders and guesswork. You’d land a trick and watch it back. And you’d be thinking, ‘How can I make that trick better?’” he said.

Alongside Jason Davenport, Google Cloud Tech Lead, they showed a model that Google Cloud built in collaboration with Google DeepMind that tracked Shaun in space from a two-dimensional video, helping him understand what he was doing right and wrong.

“Learning the trick on the mountain is one thing, but actually understanding the physics of a trick is a whole other thing,” Shaun said.

Read more on the Gemini Enterprise app here.

3. AI Hypercomputer

The same technology foundation that athletes like Shaun White use to understand their performance is being used by enterprises to transform their businesses.

Amin Vadhat, SVP and chief technologist, AI and Infrastructure, took to the stage to announce enhancements to AI Hypercomputer, the integrated supercomputing underneath every AI workload on Google Cloud.

First, there’s the eighth-generation Tensor Processing Unit, or TPU — “a thing of beauty,” Amin said. And because “the demands of training and serving have completely diverged,” this TPU family actually consists of two chips:

TPU 8t, optimized for training, uses new Inter-Chip Interconnect (ICI) technology to scale up to 9,600 TPUs and 2 petabytes of shared, high-bandwidth memory in a single superpod. It achieves three times the processing power of Ironwood and delivers up to double the performance per watt.
TPU 8i, optimized for inference, uses the new Boardfly topology to directly connect 1,152 TPUs in a single pod. It features three times more on-chip SRAM compared to previous versions and a specialized Collectives Acceleration Engine offloads resource-heavy tasks. Taken together, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, helping millions of concurrent agents to run cost-effectively.

AI Hypercomputer also supports Arm-based Google Cloud Axion processors, such as the N4A, now generally available, and will be one of the first platforms to offer NVIDIA’s Vera Rubin NVL72 platform when it is released.

Other parts of the network need to keep up with the demands of the agentic era. For example, the new Virgo Network doubles connectivity to scale training beyond AI Hypercomputer superpods, and Google Cloud Managed Lustre now supports an industry-leading 10 terabytes per second of throughput.

Learn more about all of our AI infrastructure innovations here.

4. Agentic Data Cloud

The AI era hinges on data. Lots of data. That data comes with a catch: It needs to be grounded in context. That’s because “reasoning without context is just a guess,” explained Karthik Narain, chief product and business officer.

To that end, we’re totally rethinking our data platform, and giving it a new name: the Agentic Data Cloud.

Here’s a sampling of what you’ll find in the Agentic Data Cloud:

Knowledge Catalog constructs a unified, dynamic context graph of your entire business enabling you to ground agents in all of your business data and semantics. With Smart Storage and the Object Context API, files in Google Cloud Storage are instantly tagged and enriched with metadata before an agent touches them. Knowledge Catalog is also integrated with Gemini’s Deep Research Agent.
Data Agent Kit delivers a Gemini-powered data science authoring experience across your IDEs, Notebooks, and agentic terminals.
Lightning Engine for Apache Spark is a real-time, serverless engine that is up to 4.5 times faster than open-source alternatives and offers up to double price-performance over the leading competitor for large datasets.
Finally, Cross-Cloud Lakehouse, based on Apache Iceberg, lets you query data in Amazon Web Services or Azure without having to copy it.

Learn all about all the innovations in the Agentic Data Cloud here.

5. Agentic Defense

AI makes — and demands — that everything go faster, and security operations are no exception. Increasingly, “human analysts can’t keep up with AI-driven attacks,” said Francis deSouza, COO, and president, Security Products.

“Security must become an autonomous force, responding faster than the threat itself,” he said.

To help, we introduced three new agents in Google Security Operations to help you defend at the speed of AI.

Threat Hunting agent: Helps teams proactively hunt for novel attack patterns and stealthy adversary behaviors that bypass traditional defenses. Now in preview.
Detection Engineering agent: Identifies coverage gaps and creates new detections for threat scenarios. Now in preview.
Third-Party Context agent: Enriches workflows with contextual data from third-party content. Coming soon to preview.

You can also build your own security agents with remote Google Cloud MCP server support for Google Security Operations, now generally available, and access it from the Google Security Operations chat interface, now in preview.

Then there’s Wiz, now a part of Google Cloud, whose AI-Application Protection Platform (AI-APP), Wiz Security Agents, and Wiz Workflow help you identify and respond to risks and threats at machine speed. New in the Wiz family today are:

Secure vibe-coded applications: A new integration runs Wiz security scanning directly inside the Lovable platform so vulnerabilities, secrets, and misconfigurations caught by Wiz surface in Lovable's built-in security view. Generally available in May.
Secure AI-generated code: Wiz can now remove risks from AI-generated code with inline AI security hooks integrated directly into IDEs and agent workflows, injecting security guardrails before code is committed.
Agent-based remediation: Wiz Skills can equip coding agents and AI-native IDEs with full code-to-cloud context and validated attack surface findings from the Wiz Security Graph.
AI-Bill of Materials (AI-BOM): Work towards eliminating shadow IT by automatically inventorying all AI frameworks, models, and IDE extensions across your environment.
Google Cloud Fraud Defense: The evolution of reCAPTCHA, this platform is designed to discern the legitimacy and authorization of bots, humans, and agents. Now generally available.

Read more about these security innovations here.

6. Workspace Intelligence

For a look at AI for end-users, we heard from Yulie Kwon Kim, VP, Product, Google Workspace, who shared new ways that AI is manifesting in our collaboration and productivity suite. Workspace Intelligence is a unifying semantic layer that breaks down information and context silos for you and your agents. It understands your work, your priorities and the people you work with to help you get more done.

“Think of it as a unified intelligence layer that lives inside every Workspace app. It connects the dots and lets AI do the heavy lifting," Yulie said.

Here’s what’s new:

Ask Gemini in Google Chat allows you to instantly synthesize information, surface insights, and query projects from across Workspace directly from your Google Chat window. It provides proactive daily briefings to help you prioritize, and also lets you take immediate action — such as scheduling a meeting on your calendar or creating a Google Doc to develop a pre-meeting brief — turning your conversations into momentum without having to switch tabs.

Reimagined content creation in Docs, Sheets, and Slides uses Workspace Intelligence to synthesize information from across Workspace and the web and creates professionally formatted drafts that match your voice, style, and brand.
AI Inbox and AI Overviews in Gmail creates a personal, proactive inbox assistant with Gemini.
Google Drive Projects instantly organizes your team's files and emails to manage workflows, generate content, and deliver specific answers based on rich project context. In addition to newly added AI Overviews and Ask Gemini, Projects is another way we’re transforming Drive from a storage tool into an active collaborator to provide insights about your data.
Workspace agent in Gemini Enterprise executes complex, multi-step tasks across Google Workspace apps without having to leave Gemini Enterprise.

For more, check out the full Workspace Intelligence blog.

7. Agentic Commerce

For enterprises, AI agents are reshaping how consumers engage with companies and their products.

“In the agentic era, an agent isn’t just a tool; it’s a strategic extension of your business, built to expand your reach, deepen engagement, and personalize service at scale,” said Carrie Tharp, Google Cloud Vice President of Go To Market Strategic Industries.

Gemini Enterprise for Customer Experience offers a suite of tools to enhance the entire customer journey, from the first moment of discovery through on-going interactions that remember the customer like the best shopkeeper would.

Shopping agent and Food Ordering agent bring new conversational sales and ordering capabilities direct to businesses and third-party chat and digital interfaces.
Omnichannel Gateway helps agents maintain context across web, mobile, and voice, so a company’s agents can offer more personalized service.
Agent Assist helps during complex customer service situations, coaching employees to deliver fast and more accurate answers to customer questions by having organizational data readily available through gen AI grounding.

With Omnichannel Gateway in particular, about bridging the physical, digital, and agentic shopping experience, so consumers always have a familiar, brand-aware experience.

“If a customer moves from text chat to a phone call, the agent seamlessly remembers exactly where they left off,” said Carrie. Now that’s progress!

For more customer stories, check out all 1,302 of the latest gen AI use cases from businesses around the globe.

Innovate all the things

From new products, to new solutions, to new ways of working, there are so many other ways that we’re helping organizations take their AI from pilot to production.

Over the following week, we’ll share even more news, helpful how-to guides, and go deeper on today’s announcements. Stay tuned!

Enabling the agentic enterprise: business and industry agents arrive in Gemini Enterprise

Wed, 22 Apr 2026 21:00:00 +0000

We are officially moving past the era of one-size-fits-all AI. Enterprises today require highly specialized, role-specific tools to drive real productivity — but they cannot afford to sacrifice security, oversight, or control to get there.

What's new: Today, we’re bringing partner-built agents from our Agent Marketplace directly into the Agent Gallery inside the Gemini Enterprise app. This creates a powerful, centrally governed hub for discovering and managing specialized AI from partners like Accenture, Adobe, Atlassian, Deloitte, Lovable, Oracle, Palo Alto Networks, Replit, S&P Global, Salesforce, ServiceNow, Workday, and more.

Why this matters for your business: With millions of paid seats across thousands of companies, the Gemini Enterprise app allows organizations to automate complex, end-to-end workflows in a single, unified environment. Five key benefits include:

Move beyond agentic overclaims: The market is flooded with passive chatbots and rigid workflows. Our featured Marketplace partners deliver true autonomous agents capable of executing complex, end-to-end tasks. They achieve this by combining deep context and memory, intelligent orchestration, and safe tool use.
High-caliber agents: Every featured agent earns the "Google Cloud Ready - Gemini Enterprise" designation only after passing a strict four-step evaluation for basic functionality, output accuracy, autonomous execution, and enterprise standards.
Built-in safeguards: Gemini Enterprise Agent Platform assigns every agent a cryptographically secure identity for a clear audit trail, while our Agent Gateway and Model Armor actively screen traffic to guarantee your data is never used for model training.
Strong IT governance: To balance workforce productivity with strict organizational oversight, we've built Agent Gallery around a secure, two-step governance model. Employees can browse the gallery and submit procurement or access requests for the agents that fit their needs, while IT remains firmly in the driver's seat. Administrators review all incoming requests to approve or deny deployments, retaining granular control over exactly who can interact with each agent across the organization.
A unified ecosystem breaking down silos: Typically, purchasing third-party AI means bolting on disconnected platforms. Our approach eliminates that friction. These partner agents are built to natively run within Gemini Enterprise Agent Platform. This means third-party agents and internal custom builds live side-by-side in a single, cohesive ecosystem, allowing your organization to scale its AI capabilities infinitely without ever fracturing your infrastructure.

Go-to-market opportunity for Partners: Today, we announced a $750 million partner fund for agentic development. In addition, partners can tap into a $240 billion backlog of committed enterprise spend and reach millions of Gemini Enterprise users directly in their daily workflows. We are turning everyday user discovery into a procurement flow for IT, standardizing contracts and accelerating purchasing cycles by up to 50%. With Google sales reps incentivized to work with you, and Marketplace vendors closing deals 112% larger, there has never been a better time to build with Google Cloud.

Where you can get started:

For customers: Discover these agents directly via the Agent Gallery in the Gemini Enterprise app. Start your 30-day trial today.

For partners: Take advantage of our rapid agent deployment framework, which provides a clear path to onboard, commercialize, and make agents discoverable to Gemini Enterprise users. Apply for our AI Agents Program now.

In addition, we’re launching the Google for Startups AI Agents Challenge today, including a track dedicated to startups with agents that can be primed for use in Gemini Enterprise.

Meet the agents your organization can start using today

A sampling of featured partner-built agents from Agent Marketplace accessible via the Agent Gallery in the Gemini Enterprise app

Acalvio: ShadowPlex Preemptive Cyberdefense Agent deploys honeytokens in Cloud IAM and in cloud workloads to disrupt agentic AI attacks at the reconnaissance phase. This helps secure cloud workloads from malicious or unsafe agent actions in real time.

Accenture: The Supply Chain Inventory Intelligent Advisor agent is an agentic AI-powered decision support system designed to optimize inventory planning and tracking efficiently. Using real-time Data and AI, it delivers actionable insights to balance service levels and minimize excess inventory.

Adobe: Adobe Marketing Agent for Gemini Enterprise connects natural language queries to Adobe's Customer Experience agentic capabilities. It enables instant insights on campaign performance, target audiences and journey monitoring powered by Adobe, helping teams work smarter without leaving their workflow.

Alteryx: Alteryx AI Insights Agent delivers analyst-curated insights inside Gemini Enterprise. Built on Alteryx One, analysts define trusted datasets while business users simply ask questions to receive governed, repeatable answers. Requires an Alteryx One subscription.

Ambiguous AI: Ambiguous Recruiting Coworker manages candidate communication, multi-round scheduling, and pipeline tracking from application to offer. It works natively across Gmail, Google Calendar, Google Sheets, Google Drive, and more, while looping you in for key decisions.

Amdocs: The Telco Customer Experience Agent autonomously resolves complex customer service issues across digital and contact center channels. Combining Amdocs Cognitive Core with Gemini Enterprise for CX, it reasons, orchestrates, and executes end‑to‑end telco processes, improving resolution speed, cost efficiency, and customer satisfaction.

AODocs: AODocs AI Agent provides instant, trustworthy answers grounded exclusively in the latest validated versions of controlled documents. It supports diverse use cases including standard operating procedures (SOPs), engineering specs, legal policies, and risk management procedures.

Articul8 AI: Articul8 Table Understanding Agent converts complex documents into analytics-ready table data. It reconstructs hierarchical layouts and relationships missed by traditional OCR for accurate analysis. Articul8 Diagram Understanding Agent turns complex diagrams into structured data, interpreting symbols and spatial relationships for automated analysis and seamless workflow integration.

Ascendo: Ascendo AI Agent helps critical infrastructure teams automate workflows, improve diagnostics, and reduce field escalations. By ingesting logs, telemetry, and service manuals, it recommends optimal actions to support faster, accurate decision-making in complex service environments.

Atlassian: Rovo is an AI teammate seamlessly integrated across Jira, Confluence, and your tool stack. Built on Atlassian’s Teamwork Graph, it surfaces knowledge, automates tasks, and takes intelligent actions to help teams pull context, move faster and stay aligned.

AutoCIO: Portfolio managers and investment teams use the AutoCIO Financial Forecasting Agent to design, optimize, and manage portfolios. It automates end-to-end workflows, delivers predictive signals across 50,000+ securities, and enables real-time, hyper-customized strategies via natural language prompts.

Avalara: Avi Agent is an AI-powered gateway for tax and compliance automation. Built on Avalara’s Agentic platform, it smartly coordinates between external systems and compliance agents to instantly and securely calculate taxes, file returns, and manage invoicing.

Backstory: Backstory Revenue Answers Agent tells revenue leaders what's happening on any account, identifies deal risks, and suggests actions. It achieves this by synthesizing CRM data, engagement activity, stakeholder relationships, and public company news into actionable insights.

Botcopy: TrueQ is an AI analyst for government contact centers, enabling supervisors to understand and improve conversational AI performance in the Gemini Enterprise app. It identifies conversation patterns, escalation triggers, and knowledge gaps to improve automation, strengthen governance, and reduce support costs.

Carto: Carto Site Selection AI Agent helps enterprises analyze and visualize prospective commercial locations using spatial analytics. Users ask conversational questions about foot traffic, demographics, or competitors, and results automatically fuel a comprehensive location analysis.

Corvic AI: Corvic AI Agent connects to your enterprise data and delivers production-ready decision intelligence — no pipelines and no data engineering required. Use cases include turning competitive signals and market trend data into actionable intelligence, root cause analysis and customer support automation.

Cotality: The Cotality Payoff Analysis Agent helps mortgage lenders improve customer retention and reduce loan runoff. By analyzing loan originations and payoffs, it delivers actionable intelligence to understand recapture performance, borrower behavior, and competitive insights.

Crescendo Lab Ltd.: DAAC AI Agent transforms complex data into actionable growth strategies for marketing and sales. Using natural language, it delivers instant conversational insights and automated dashboards, effectively eliminating manual reporting and guesswork.

Deloitte: Tariff Management Agentic Suite automates customs regulatory workflows via sub-agents that ingest documentation, extract attributes, and reconcile tariffs against filings. It flags discrepancies to ensure full audit coverage, eliminate manual effort, and recover unnoticed costs.

Devoteam: Demand Sensing Agent helps leaders improve forecast accuracy and optimize inventory. It analyzes sales and real-time market signals to provide immediate demand insights, enabling proactive adjustments. Request for Proposal Agent helps sales teams draft proposals, leveraging centralized knowledge to rapidly generate tailored and compliant proposal drafts.

Dun & Bradstreet: The Dun & Bradstreet Business Verification Agent enables trusted business verification in the Gemini Enterprise app. Powered by Dun & Bradstreet’s trusted data, it resolves businesses to a D-U-N-S® Number and validates identity attributes, enabling front-line teams to move faster and focus on exceptions.

Dynatrace: Dynatrace for Gemini Enterprise integrates AI agents with Dynatrace observability data via Gemini Enterprise Agent Platform and A2A protocol. This enables real-time insights into performance, infrastructure, and user experience, seamlessly enhancing AI workflows while ensuring enterprise-grade security and compliance.

Enigma: Enigma KYB Agent gives compliance teams instant, AI-driven business verification backed by ground-truth business identity from 100M+ registrations and authoritative government records, plus sanctions watchlists and real-time transaction signals.

Eon: Eon AI Agent empowers cloud infrastructure teams to streamline data protection operations. It connects Google Cloud accounts, triggers resource discovery, monitors protected resources, tracks backup jobs, and inspects restore status — all through natural conversation, replacing manual console workflows.

EPAM: Creative Conductor Marketing Agent turns creative briefs into polished promotional videos. It coordinates specialized sub-agents for visuals, voiceover, and music, assembling assets into a final MP4 to deliver fast, scalable, high-quality marketing content.

Exa AI: Exa Agent gives workers a live web view, turning it into a searchable database of code, people, and companies. It searches billions of pages to accelerate tasks like developer tooling, recruiting, and market mapping.

Foxtrot Communications: Forge - AI Agent helps teams eliminate manual ETL pipeline development. Connect any API, and Forge autonomously delivers clean, normalized, analytics-ready datasets, reducing time-to-insight to minutes and freeing engineers to focus on strategic initiatives.

Genpact (UK) Ltd: Genpact Finance One – Revenue Lens and PnL Agents are a suite of agents built on Google ADK and the A2A protocol, enabling finance users to gain actionable insights from revenue and profit & loss data through natural language conversations in Gemini Enterprise.

Genspark: Genspark AI Agent turns multi-step work into finished outputs with one prompt by combining search, web interaction, content generation, and media creation. It helps users execute research, create documents, and automate workflows through a single agent built for real work.

HCLTech: HCLTech ITOps Agent is built on Google Cloud and the HCLTech AI Force platform and enables autonomous IT operations within ServiceNow. Using natural language, it triages incidents, surfaces relevant SOPs, and drives guided remediation to improve overall efficiency, consistency, and operational performance.

Invideo: Invideo empowers marketing teams to scale video production while maintaining consistency. By leveraging brand guidelines and assets, it delivers production-ready ads and short films with built-in quality controls and output in 50+ languages.

Iron Mountain, Inc.: The Iron Mountain InSight® DXP AI Agent bridges Gemini Enterprise and your managed content for unified search and AI-powered Q&A. Its intended purpose is to accelerate results and reduce hours of manual document review to seconds of conversational inquiry.

Lilt: LILT Assist empowers global content managers to transition from manual tasks to autonomous orchestration, managing end-to-end multilingual production and brand governance. Assist scales global content volumes with AI while ensuring enterprise-grade consistency and compliance.

Lovable: Lovable empowers anyone to build real apps and websites by chatting with AI. Teams across product, design, marketing, sales and operations can turn ideas into real, working software.

LTM: LTM Video Intelligence Agent is based on Google Cloud AI technology and converts recorded videos into BDD test cases in a standard format for faster time-to-test and improved productivity.

LumApps: The LumApps AI Agent helps organizations retrieve company knowledge and announcements, trigger workflows, and complete tasks across enterprise systems.

Manhattan: Manhattan Active Agents are AI digital teammates for warehouse management, transportation management, order management, and platform operations. These agents help monitor work, resolve exceptions, guide users, automate tasks, and recommend actions to reduce manual effort; improving speed and accuracy, increasing utilization, and delivering better operational resilience.

Menlo Security: The Menlo Security Orchestrator for Gemini Enterprise reduces mean time to repair (MTTR) from hours to milliseconds. Command global policy and perform deep threat reporting through natural conversation in the Gemini Enterprise app.

monday.com: monday.com agent in Gemini Enterprise turns planning into execution. Teams can quickly build boards, summarize updates, and trigger automations without leaving their workspace—keeping everyone aligned, tracking progress, and driving measurable results.

Nativeorange: LexAI Agent streamlines property and casualty (P&C) underwriting and operations. It ingests submissions, extracts data, and automates risk evaluation, delivering actionable insights on classification and offering next steps to improve speed, accuracy, and decision consistency.

Neo4j: Neo4j Agent translates natural language into Cypher, enabling applications to read from and write to the Neo4j database. Integrated with Gemini Enterprise, it can be used to build agents grounded with GraphRAG and connected enterprise knowledge.

Obin AI: Obin Financial Agent helps private markets and commercial lending teams run financial analysis from Gemini Enterprise. Users submit model spreadsheets and deal-related documents with a scenario description in plain-language. The agent then extracts relevant data and computes financial metrics across modeling assumptions.

Onix: The Onix Risk & Compliance Intelligence Agent automates compliance reviews and risk assessments across regulated industries. It reduces review times from weeks to minutes, proactively identifies regulatory risks, and creates detailed audit trails with citations.

OpenText: OpenText™ Content Aviator™ is a generative AI assistant that enhances content search and productivity. Built on Gemini Enterprise Agent Platform, it allows users to perform natural language searches and efficiently summarize enterprise documents.

Oracle: Oracle AI Database Agent lets you query Oracle data directly from Gemini Enterprise. Available via Google Cloud Marketplace, it delivers fast, enterprise-grade answers without requiring custom chatbots or NL-to-SQL maintenance.

Orion by Gravity: Orion by Gravity is an AI analyst that connects to Looker, BigQuery, and more to proactively hunt for insights. It empowers data teams to identify anomalies and recommend actions that uncover 'unknown-unknowns' in analytics-rich industries.

Palo Alto Networks: Prisma AIRS Model Security agent scans over 35 model formats to eliminate malicious code hidden deep within model layers. This proactive defense goes beyond standard scanning to ensure neural integrity, detecting poisoned weights and backdoors to prevent unauthorized data exfiltration and model hijacking.

Pendo: The Pendo Agent connects your product intelligence to Gemini Enterprise. Query usage trends across SaaS and AI tools, surface adoption gaps, and understand the impact of your AI investments without leaving the Gemini Enterprise app.

Persistent Systems: Ethical Content Auditor Agent helps editors at publishing houses audit political and social content for potential biases and risks. Editorial Assistant Agent enables efficient manuscript evaluation by reading and understanding long manuscripts, analyzing market trends and providing data-backed summaries and recommendations.

Pluto7: Pluto7’s Planning in a Box - Pi Agent orchestrates 50+ sub-agents for supply chain and manufacturing teams. This enables self-healing, autonomous supply chains that optimize demand and production in real-time.

Product Genius: Product Genius brings AI-powered ecommerce intelligence directly into Gemini Enterprise. Share your website URL to get instant insights, actionable policies, and a conversational analyst that helps you optimize your store—no code install required.

Quantum Metric: Felix Agentic for Gemini Enterprise delivers instant insight, contextualized in business objectives, to digital leaders and analytics teams. Leveraging Gemini models, Felix continuously analyzes your digital experience to explain what changed, why it matters, and where it’s costing you.

Red Hat, LLC: Red Hat Lightspeed Agent for Google Cloud helps SREs and admins manage Red Hat Enterprise Linux infrastructure on Google Cloud via natural language. Built on A2A and Gemini, it securely connects with Red Hat Lightspeed services to streamline enterprise IT operations on Google Cloud.

Replit: Replit Agent enables business users to build enterprise-grade applications using natural language. With no coding knowledge required, users turn complex requirements—like a BigQuery-backed customer health dashboard—into functional custom tools in minutes.

S&P Global: The S&P Global Data Retrieval Agent by Kensho enables financial professionals to access cited data from S&P Global's proprietary datasets. It allows analysts and portfolio managers to analyze earnings calls, conduct market research, and instantly retrieve specific financial metrics.

Salesforce: Agentforce Sales for Gemini Enterprise can engage leads, create meeting briefs, surface deal risks and guidance, and manage pipeline and CRM updates — securely and in real-time — without leaving Gemini Enterprise. With the agent handling the grind of manual tasks, sellers can focus on relationship building and strategic selling to win.

Sana from Workday: Sana Self-Service Agent instantly finds and summarizes information from Workday and other knowledge sources, providing personalized answers and assistance with tasks. With over 300 skills across areas like pay, time, and absence, the agent handles everyday HR and finance tasks globally, reducing support tickets and allowing teams to focus on high-value work.

Saviynt: Identity Security for AI enables continuous discovery and inventory of agents, identifying their functions, access, and risks. It treats agents as first-class identities with defined purposes and accountable owners. This blend of posture visibility and enforced accountability converts opaque automation into governable, enterprise-ready identities.

ServiceNow: Now Assist for IT Operations Management operates inside the ServiceNow AI Platform, harnessing the power of GenAI to elevate operational intelligence and automation across IT environments. It introduces a suite of AI-driven capabilities designed to optimize every stage of alert and incident management.

Sheetgo: Sheetgo is an AI-ready data execution platform that transforms spreadsheet-driven operations into governed, reliable pipelines for BigQuery and Gemini Enterprise.

Skyflow: The Skyflow Runtime Data Security Agent helps teams securely deploy agents on Gemini Enterprise Agent Platform. It provides expert guidance and on-demand data protection with fine-grained access controls, enabling enterprises to meet compliance without risking privacy.

Supermetrics: The Supermetrics Marketing Intelligence Agent delivers live, cross-channel performance insights directly in Gemini Enterprise. Connecting to 175+ sources like Google Ads and GA4, it provides fast, hallucination-free analysis using simple natural language prompts.

Synthpop Inc.: The Synthpop Patient Journey Orchestration Agent turns unstructured referrals into validated, payer-ready orders in real time by executing intake and patient-facing workflows. These flows help payers save time as part of Synthpop’s coordinated, production-grade administrative backbone.

Tech Mahindra: Frontline agents use Order Assist to resolve complex orders and service cases faster. By delivering real-time, contextual intelligence through conversation, it reduces handling time, improves first-contact resolution, and enables consistent, data-driven responses.

Teradata: Data Analyst AI Agent enables natural-language analytics on enterprise data in Google Cloud. It translates business questions into governed SQL and Python workflows on Teradata, delivering faster, secure insights without requiring data movement.

Typeface: Typeface Agent acts as a seamless bridge to creative campaigns. It enables instant access to Typeface teams, brand assets, and campaigns via natural language from Gemini Enterprise. Users can trigger high-quality, brand-aligned content creation workflows, streamlining the journey from ideation to production.

UKG: Agentic People Assist — HR Service Delivery elevates the agentic capabilities of the UKG Workforce Operating Platform by empowering employees and managers with knowledge and connected agents to turn intent into action. It orchestrates workflows across time, pay, and HR to deliver real-time, personalized, and measurable outcomes.

WRITER: WRITER Agent is autonomous AI for Fortune 500 enterprises. Built for high-stakes enterprise workflows, it safely plans and executes autonomous work across data and tools based on company context, from brand standards to repeatable playbooks.

XM Cyber: XM Cyber’s PostureAI agent serves as an autonomous posture specialist, providing continuous assessment of Google Workspace applications against security best practices. It is part of a broader Continuous Exposure Management offering that allows organizations to safely adopt AI and secure their critical business processes.

Together, we are creating the world's largest ecosystem of agents — one that will redefine every industry, supercharge every function, and accelerate every business process. I cannot wait to see what we all achieve together.

What’s new in GKE at Next ‘26

Wed, 22 Apr 2026 12:00:00 +0000

This week at Google Cloud Next ‘26, we are sharing the evolution of Google Kubernetes Engine (GKE), delivering leading performance, efficiency, security, and scale for your most demanding and complex workloads, and the next generation of AI and agentic applications.

Why it matters: Kubernetes has rapidly become the operating system for the AI era, with GKE now powering AI workloads for all of our top 50 customers on the platform, including the largest frontier model builders. We are witnessing a massive acceleration in enterprise AI. In just a few months, the number of multi-agent AI workflows has surged by 327%. At the same time, 66% of organizations rely on Kubernetes to power generative AI apps and agents.

This new era of autonomous agents operating at massive scale requires a foundational change in how we manage infrastructure — a change that is more demanding than the shift from stateless to stateful applications.

What’s new:

GKE Agent Sandbox: Secure, highly scalable, low-latency agent infrastructure
GKE hypercluster: A single, conformant GKE control plane to manage millions of accelerators across Google Cloud regions
Improved inference performance: Foundational enhancements to GKE Inference Gateway and KV Cache management
Reinforcement learning (RL) enhancers: Native capabilities to relieve bottlenecks that throttle accelerator utilization
Scaling on custom metrics: Support for intent-based autoscaling on triggers besides CPU and memory

Read on for details about these GKE announcements.

GKE Agent Sandbox: Accelerating the agentic era

As AI evolves from simple conversational chatbots to entire ecosystems of proactive, autonomous agents, the underlying infrastructure must adapt to handle hundreds or thousands of agents collaborating with workers to plan, evaluate, and execute complex tasks. At scale, infrastructure performance, responsiveness, and rigorous security are essential.

We are excited to announce GKE Agent Sandbox, the industry’s most scalable and low-latency agent infrastructure. Built with gVisor kernel isolation — the same technology securing Gemini — Agent Sandbox allows you to safely execute untrusted code, tools, and entire agents without sacrificing performance. GKE provides leading speed and efficiency for fully isolated agents with 300 sandboxes per second at sub-second latency and up to 30% better price-performance when running on Axion compared to other hyperscale clouds.

Lovable empowers anyone to build apps and websites — with builders creating 200,000+ new projects daily. Lovable runs these AI-generated applications in GKE Agent Sandboxes because of the fast startup, fast scaling and secure isolation.

GKE's cutting-edge sandboxing capabilities allow us to reliably scale to hundreds of secure sandboxes per second, ensuring we can seamlessly empower builders, even during massive, unpredictable demand." - Fabian Hedin, Co-founder, Lovable

GKE hypercluster redefines the scalability ceiling

As foundational AI models grow exponentially and accelerators remain in high demand, organizations resort to fracturing Kubernetes compute infrastructure into hundreds of disconnected clusters, which can create a massive operational burden. To help, we’re announcing the private GA of GKE hypercluster, which allows a single, Kubernetes conformant GKE control plane to manage a million chips distributed across 256,000 nodes — spanning multiple Google Cloud regions. With the GKE hypercluster, widely distributed infrastructure becomes a single, unified capacity reserve that spans geographical locations.

To scale globally without compromising security, GKE hypercluster relies on Google’s Titanium Intelligence Enclave, a software-hardened security engine that delivers private AI compute. This "no-admin-access" model provides hardware-attested, pod-level isolation, so that proprietary model weights and prompts remain cryptographically sealed from platform administrators and infrastructure layers.

Supercharging state-of-the-art inference

Achieving frontier inference requires months of complex performance tuning. To reduce this heavy lifting, GKE now slashes your "time to SOTA" across TPUs and GPUs to mere minutes. We do this with new capabilities:

ML-driven Predictive Latency Boost in GKE Inference Gateway, which can reduce time-to-first-token latency by up 70% by replacing heuristic guesswork with real-time capacity-aware routing — no manual tuning required.
Automatic KV Cache storage tiering across RAM, Local SSD, and GCS/Lustre solves long-context memory bottlenecks. Offloading KV Cache to RAM yielded a more than 40% TTFT reduction and a 50% throughput gain for a 10K system prompt length. Offloading KV Cache to Local SSD yielded an almost 70% throughput improvement for a 50K system prompt length. Learn more about these benchmarks in the llm-d Offloading Prefix Cache to Shared Storage guide.

Built as part of a layered composable suite, these new GKE capabilities leverage llm-d, now an official CNCF Sandbox project. To give you maximum flexibility, we’ve partnered closely with NVIDIA to seamlessly integrate Dynamo for scaling massive Mixture-of-Experts (MoE) models. Whichever tools you choose, GKE provides the highly-optimized, flexible infrastructure you need to safely run any frontier AI workload — including the advanced agentic capabilities of the newly announced Gemma 4.

Eliminating RL compute bottlenecks

Reinforcement learning (RL) is a key driver of AI compute demand and RL jobs involve sequential processing for sampling, reward, and training that can leave GPU and TPU accelerators idle between these RL steps. To streamline RL, we are adding new GKE capabilities in preview:

RL Scheduler solves for the "straggler effect" and inter-batch tail latency, maximizing throughput via intelligent routing.
RL Sandbox provides kernel-level isolation for tool-calling and reward evaluation with millisecond-scale provisioning. Easy integration with RL sampling and reward steps.
RL Observability and Reliability dashboards offer the deep visibility required to troubleshoot and optimize the entire RL loop instantly, out of the box.

Review the RL on GKE recipe, specifically the implementations for Verl and NeMo RL.

Intent-based autoscaling on custom metrics

Traditionally, scaling AI workloads based on application health has imposed a "custom metric tax." To scale the system on anything but basic compute or memory utilization, organizations have to manage complex monitoring systems and IAM roles. This creates operational risk: if your external observability stack fails, your autoscaling breaks along with it.

Intent-based autoscaling eliminates this overhead via native custom metrics support for GKE’s Horizontal Pod Autoscaler (HPA). This agentless architecture bypasses external dependencies by sourcing metrics directly from Pods, hardening reliability while cutting costs. Crucially, it drops reaction times from 25 seconds to just 5 seconds—a 5x performance gain for near-instantaneous infrastructure elasticity.

New workloads, same mission

For over a decade, GKE has set the standard for scalable infrastructure. As we enter the era of agentic and autonomous AI, our mission remains the same: eliminating operational friction so you can focus on innovation. The capabilities we are announcing at Next ‘26 — from GKE hypercluster and the Agent Sandbox, to ultra-fast inference and intent-based autoscaling — give you the secure, efficient, and powerful engine you need to succeed with your ambitious AI workloads. To learn more about using GKE for your AI workloads, check out GKE Inference Quickstart.

Introducing Virgo Network, Google’s scale-out AI data center fabric

Wed, 22 Apr 2026 12:00:00 +0000

The AI era requires a fundamental rethink of physical cloud architecture — networking, in particular. With foundational model parameters growing exponentially, traditional general-purpose networks are reaching their breaking points. To fuel the next decade of machine learning, Google designed Virgo Network, a new megascale AI data center fabric that embraces a "campus-as-a-computer" philosophy, and that underpins our AI Hypercomputer.

Legacy network designs simply cannot handle some of the constraints of modern AI:

Massive scale: Training demands now exceed the power and space of a single data center, requiring unified, multi-data-center domains.
Explosive bandwidth growth: Because foundational model training is heavily network-bound, the required bandwidth per accelerator has surged significantly over the last few years, creating throughput and congestion bottlenecks for older architectures.
Synchronized bursts: Intense, millisecond-level traffic spikes (figure 1) put immense pressure on network buffers. The outcome is that even a single "straggler" node can throttle the entire cluster’s performance.
Low latency: ML serving requires fast, consistent response times to deliver real-time inference, making strict latency control a critical architectural constraint.

Figure 1: Sub-millisecond line-rate bursts of an AI training workload

Reimagining the data center network

Meeting the demands of the AI era requires a fundamental shift away from general-purpose network design towards a specialized flat, low-latency network architecture. To address the unique scale and latency constraints, we leverage our proven Jupiter network for north-south traffic and are introducing a new fabric for east-west communication. The resulting architecture consists of three distinct and specialized layers that operate as one unified compute domain:

Scale-up domain: A high-bandwidth, low-latency interconnect fabric designed for tightly coupled communication between accelerators within a single pod.
Scale-out accelerator fabric (east-west): A dedicated accelerator-to-accelerator remote direct memory access (RDMA) fabric optimized for massive horizontal scale across pods. This layer is engineered for deterministic latency and maximum resilience, to provide high “goodput” for the ML workload.
Jupiter front-end network (north-south): A high-capacity fabric that provides fast, reliable access to distributed storage and general-purpose compute resources. It ensures that data access does not become a bottleneck for training and serving workloads, and is also used to scale-across multiple sites for very large training runs.

This architectural decoupling provides key strategic advantages:

Independent evolution: We can evolve and upgrade each network domain independently, preventing system-wide disruptions while accelerating the innovation cycle.
Dedicated scale-out bandwidth: A non-blocking network delivers massive bisectional bandwidth to accelerators for critical training tasks.
ML and network co-design: The network is built in lockstep with each new generation of ML accelerators, helping ensure the fabric is matched to the hardware it supports.

Figure 2: Data center network architecture

Introducing Virgo Network: Megascale data center fabric

Virgo Network is a scale-out fabric designed for the extreme requirements of modern AI workloads. Built on high-radix switches that reduce network layers by allowing more ports per switch, it employs a flat, two-layer non-blocking topology. Compared with traditional datacenter networks, this significantly reduces latency by minimizing network tiers. It features a multi-planar design with independent control domains to connect accelerators (figure 3). The accelerator racks also connect with the Jupiter north-south fabric to access compute and storage services. Together, this streamlined architecture delivers the massive bisection bandwidth and deterministic low latency necessary for both distributed training and serving workloads.

Figure 3: Megascale data center fabric (Virgo Network)

Virgo Network is the foundation of our next-generation accelerator designs and delivers the following advantages:

Massive fabric scale: Virgo Network can link 134,000 chips (TPU 8t) with up to 47 petabits/sec of non-blocking bi-sectional bandwidth in a single fabric.
Generational performance leap: With up to 4x the bandwidth per accelerator (TPU 8t) over the previous generation, Virgo Network delivers the bandwidth you need to get the full power of every chip.
Predictable low latency: Virgo Network delivers 40% lower unloaded fabric latency for TPUs compared to previous generation leading to more predictable performance for latency sensitive AI workloads.

Improving reliability at scale

In a system supporting hundreds of thousands of chips, hardware failures are inevitable. Because a single faulty component can disrupt a synchronized training job, reliability at scale is a primary focus. To maximize workload goodput, we designed the Virgo Network architecture around fault isolation, deep observability, and the rapid mitigation of hangs and stragglers.

At this scale, system-wide resilience requires a solid network foundation. Virgo Network integrates independent switching planes that provide robust fault isolation, protecting cluster-wide goodput from being degraded by localized hardware failures.

Figure 4: How fail-stop and fail-slow impact MTTR

Building on this foundation, we optimize the software and orchestration stack to maximize mean-time between interruptions (MTBI) and minimize mean-time to recovery (MTTR) through two primary areas:

Observability: Reliability at scale requires high-fidelity visibility. We use sub-millisecond telemetry to monitor network systems. This deep visibility allows us to detect transient congestion, optimize buffer management, and pinpoint the root causes of slowdowns across the hardware and software stack.
Identifying stragglers and hangs: Proactive monitoring is critical for identifying nodes that are experiencing performance degradation (stragglers) or that have stopped responding completely (hangs). By rapidly localizing these bottlenecks, with automated straggler and newly added hang detection, we accelerate the training job and protect it from localized slowdowns.

The foundation of the AI Hypercomputer

Virgo Network is a reimagined scale-out data center network custom-built for the stringent demands of modern AI workloads. This flat, multi-planar architecture unifies accelerators across pods into a single compute domain, addressing the bandwidth and scale limitations of traditional networks. By providing robust fault isolation directly at the hardware level, Virgo Network serves as the foundation for system-wide resilience, protecting synchronized workloads from localized hardware faults.

Ultimately, Virgo Network delivers the scale, predictable latency, and reliability necessary to accelerate the agentic AI era. To learn more about how we are building infrastructure for the future of AI, visit our AI infrastructure solutions page, explore the technical documentation, or attend the dedicated breakout session at Google Cloud Next.

Introducing Gemini Enterprise Agent Platform, powering the next wave of agents

Wed, 22 Apr 2026 12:00:00 +0000

In the early days of generative AI, building safe and reliable business tools took massive engineering effort and a high tolerance for trial and error. We helped solve that with Vertex AI, our trusted AI development platform. But today, we’re managing a different level of complexity, with agents interacting across multiple systems — and often without security and governance guardrails.

To move toward a truly autonomous enterprise, one where agents can act with the same independence and reliability as a member of your team, you need a foundation that can sustain that level of trust.

What’s new: Today, we’re launching Gemini Enterprise Agent Platform — our new, comprehensive platform to build, scale, govern, and optimize agents. It’s the evolution of Vertex AI, bringing the model selection, model building, and agent building capabilities that customers love, together with new features for agent integration, DevOps, orchestration, and security.

Agent Platform provides a single destination for your technical teams to build agents that can transform your products, services, and operations. These agents can be seamlessly delivered to your employees through the Gemini Enterprise app, all while remaining tightly integrated with your IT operations to help ensure control, governance, and security as you scale.

The platform also provides first-class access to more than 200 of the world’s leading models through Model Garden. This includes our latest first-party breakthroughs like Gemini 3.1 Pro, Gemini 3.1 Flash Image, and Lyria 3, alongside our open models like Gemma 4. And, of course, customers have full flexibility to use the best model for the job with support for third-party models like Anthropic’s Claude Opus, Sonnet and Haiku.

Moving forward, all Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform, rather than as a standalone service, to power the next generation of agent development.

Why Agent Platform matters for your business: Agent Platform helps you move from managing individual AI tasks to delegating business outcomes with total confidence. You can:

Build: Choose the right environment for the job — from the low-code, visual interface of the new Agent Studio, to the code-first logic of the upgraded Agent Development Kit (ADK). We’ve simplified the entire lifecycle with AI-native coding capabilities to help you ship production-grade agents faster.
Scale: Clear the path to production with the re-engineered Agent Runtime. This supports long-running agents that maintain state for days at a time and are backed by Memory Bank for persistent, long-term context.
Govern: Establish centralized control with Agent Identity, Agent Registry, and Agent Gateway. These capabilities help ensure every agent — whether built on Agent Platform or sourced from our partner ecosystem — has a trackable identity and operates within enterprise-grade guardrails.
Optimize: Guarantee quality with Agent Simulation, Agent Evaluation, and Agent Observability. These tools provide full execution traces and a real-time lens into agent reasoning to help ensure your agents always hit their goals.

Get started with Agent Platform: Visit Agent Platform in the Google Cloud console to explore new features and start building today.

Keep reading for a deeper look at our latest releases and how Agent Platform helps you deliver the production-ready agents you can trust at every stage of the journey.

How customers are achieving more with Gemini Enterprise Agent Platform

"Burns & McDonnell uses Agent Platform to transform how organizational knowledge is applied across the enterprise. Using ADK, we are building an AI agent that turns decades of project data into real-time, actionable intelligence. Agent Platform enables this innovation to scale responsibly by combining deterministic business rules with probabilistic reasoning — making AI a trusted operational capability, not just a productivity tool. With Agent Platform, we aren’t just managing knowledge; we are activating experience to drive faster, more confident decisions." – Matt Olson, Chief Innovation Officer, Burns & McDonnell

“Color Health uses Agent Platform to power our Virtual Cancer Clinic, delivering end-to-end care. By building our Color Assistant with the Agent Development Kit (ADK) and scaling it via Agent Runtime, we are helping more women get screened for breast cancer. The Color Assistant engages users to check screening eligibility, connects them to clinicians, and helps schedule appointments. The power of the agent lies in the scale it enables — helping us reach more people and respond to individual risk and eligibility in real time.” – Jayodita Sanghvi, PhD., Head of AI Platform, Color

“By rebuilding Comcast’s Xfinity Assistant with Agent Development Kit (ADK), we’ve moved beyond simple scripted automation to conversational generative intelligence that delivers personalized troubleshooting and self-service support to our customers. Agent Runtime has been a massive accelerator, allowing us to deploy a sophisticated multi-agent architecture that increases digital containment while ensuring secure, grounded interactions via Gemini. We aren't just reducing repeat interactions by solving customers’ issues the first time; we're redefining the customer experience at scale.” – Rick Rioboli, Chief Technical Officer, Connectivity & Platforms, Comcast

“Geotab uses Agent Platform to rapidly accelerate our AI Agent Center of Excellence. Google's Agent Development Kit (ADK) provides the flexibility to orchestrate various frameworks under a single, governable path to production, while offering an exceptional developer experience that dramatically speeds up our build-test-deploy cycle. For Geotab, ADK is the foundation that allows us to rapidly and safely scale our agentic AI solutions across the enterprise” – Mike Branch, Vice President, Data & Analytics, GeoTab

"Gurunavi uses Agent Platform to power 'UMAME!', an AI restaurant discovery app that leverages Memory Bank to achieve a deep understanding of user context. Unlike conventional prompt-based systems, our agent remembers a user's past actions and preferences to proactively present the best options. This eliminates the need for manual searches and creates a seamless experience that will improve user satisfaction by 30% or more. We view this memory function as a non-negotiable feature for the future of new culinary experiences.” – Toshiaki Iwamoto, CTO, Gurunavi

"At L'Oréal, Beauty Tech is not just a support function — it is a powerful catalyst to create the beauty that moves the world. To live up to that ambition, we decided to build our own proprietary Beauty Tech Agentic Platform, powered by Google Cloud. Leveraging Agent Development Kit (ADK), we are leading a fundamental shift: moving from deterministic workflow automation to autonomous, outcome-oriented agent orchestration. Our agents are not locked in a vacuum — through Model Context Protocol (MCP), they are securely connected to our single sources of truth, including our Beauty Tech Data Platform and core operational applications. Google Cloud gives us the resilience, the multi-LLM flexibility, and the enterprise-grade trust framework we need to scale this platform globally, while keeping human oversight at the center." – Etienne BERTIN, Group CIO, L'Oréal

“Payhawk uses Agent Platform to transform our AI agents from simple task executors into genuine financial assistants. By leveraging Memory Bank, we have moved from stateless interactions to long-term context retention. Our agents now act like dedicated team members, autonomously recalling user-specific constraints and history. For example, our Financial Controller Agent now remembers a user’s habits to auto-submit expenses, reducing submission time by over 50%. This shift allows our agents to anticipate needs based on past behavior rather than just reacting to prompts.” – Diyan Bogdanov, Principal Applied AI Engineer, Payhawk

"PayPal uses Agent Platform to rapidly build and deploy agents in production. Specifically, we use Agent Development Kit (ADK) and visual tools to inspect agent interactions, and manage multi-agent workflows. This provides the step-by-step visibility we need to visualize the flow of intent and payment mandates. Finally, Agent Payment Protocol (AP2) on Agent Platform provides the critical foundation for trusted agent payments. helping our ecosystem accelerate the shipping of secure agent-based commerce experiences." – Nitin Sharma, Principal Engineer, AI, PayPal

Build AI agents

Build agents quickly and easily by empowering your developers, business users and everyone in between to build and deploy agents at scale.

Build smarter agents, faster

A major upgrade to ADK: More than six trillion tokens are processed monthly on Gemini models through ADK. Unlock more powerful reasoning by organizing agents into a network of sub-agents. This new, graph-based framework allows you to define clear, reliable logic for how agents work together to solve complex problems.
Workspaces are secure-by-design: Give agents a hardened, sandboxed environment to run bash commands and manage files safely, isolated from your core systems.
Multimodal streaming: Bring human-like stability to real-time interactions with multimodal support for live audio and video cues.

Connect your agents to the enterprise

Securely access any system: Use plug-and-play architecture with Native Ecosystem Integrations to connect agents to your internal data and tools without custom coding.
Automate background operations: Activate your data in BigQuery and Pub/Sub with Batch & Event-driven agents. This way, you can run massive, asynchronous tasks like content evaluation or data analysis in the background.

Go from idea to production in hours

Enable AI-driven development: A programmatic interface for coding agents to access Google’s complete suite of agentic capabilities, allowing them to build, evaluate, and deploy production-ready agents on your behalf.
Bringing agent building directly to Agent Studio: Now, you can move seamlessly from building simple prompts to deploying complex agents in Agent Studio. Once you're ready for deep customization, export your logic directly into ADK to continue development in a full-code environment.
Get a head start with pre-built agents: Access a curated set of agent templates in Agent Garden — including code modernization, financial analysis, economic research, invoice processing, and more — that serve as immediate building blocks for your multi-agent systems.

Scale AI agents

To move from a proof-of-concept to a live environment, you need a platform that can handle the performance, state, and security requirements of real-world work.

Powering high-performance agent execution

The latest Agent Runtime: Our revamped Agent Runtime delivers sub-second cold starts and allows you to provision new agents in seconds.
Support for multi-day workflows: You can now deploy long-running agents that run autonomously for days at a time. This allows your agents to manage complex, multi-step workflows and deep reasoning tasks that require extended persistence, like managing a sales prospecting sequence.
Autonomous action with security-by-design environments: Agent Sandbox provides a hardened environment to safely execute model-generated code and perform computer use tasks like browser-based automation without risk to your host systems.
Agent-to-agent orchestration: Enables agents to seamlessly delegate tasks to one another, including support for complex, generative, and deterministic orchestration patterns. This ensures that for critical flows such as compliance, your agents follow well-specified paths every time.

Move beyond temporary session data to high-accuracy context

Personalize interactions: Agent Memory Bank dynamically generates and curates long-term memories from conversations. Using new Memory Profiles, agents can recall high-accuracy details with low latency, ensuring context is never lost.
Link AI interactions to your existing records: Store and manage history using Agent Sessions. With Custom Session IDs, you can use your own unique identifiers to track sessions and map them directly to your internal database and CRM records.
Enable real-time, human-like interactions: Using the WebSocket protocol for Bidirectional Streaming, you can help ensure your agents are highly responsive during live customer or employee interactions, processing audio and video without lag.

Govern AI agents

Govern with a secure-by-design architecture that applies enterprise rigor to every agent in your fleet – from the ones you build on Agent Platform to the ones you source from our partner ecosystem.

Manage all of your agents through a single source of truth for identity and access.

Assign every agent a verifiable identity: Agent Identity improves the security posture of your agents by ensuring every agent receives a unique cryptographic ID. This creates a clear, auditable trail for every action an agent takes, mapped back to defined authorization policies.
Maintain a central library of approved tools: Our new Agent Registry provides a single source of truth for your enterprise. It indexes every internal agent, tool, and skill, simplifying discovery and ensuring only governed, approved assets are available to your users.
Manage your agent fleet from one control point: Agent Gateway acts as the air traffic control for your agent ecosystem. It provides secure, unified connectivity between agents and tools across any environment, while enforcing consistent security policies and Model Armor protections to safeguard against prompt injection and data leakage.

Use AI-powered insights to detect hidden risks and suspicious behavior before they impact your business.

Detect suspicious behavior in real-time: Agent Anomaly Detection uses statistical models and an LLM-as-a-judge framework to flag unusual reasoning. This works alongside Agent Threat Detection to provide visibility into malicious activity, such as reverse shells or connections to known bad IP addresses.
Uncover vulnerabilities automatically: A new Agent Security dashboard, powered by Security Command Center, unifies threat detection and risk analysis. It allows your teams to map relationships between agents and models, automate asset discovery, and scan for vulnerabilities in the underlying operating system and language packages.

Optimize AI agents

Agent Platform gives you the visibility needed to understand how your AI is performing, making it easy to refine their logic and get smarter over time.

Test your agents before they ship

Simulate realistic conversations: Use Agent Simulation to test agents against human-like synthetic user interactions and virtualized tools in a controlled environment. Agents are automatically scored based on task success and safety across multi-step conversations.

Monitor and improve in production

Track live performance: Use Agent Evaluation to continuously score agents against live traffic using multi-turn autoraters that can evaluate the logic of an entire conversation, not just a single response. With turnkey dashboards and Agent Observability, you can visually trace complex reasoning to debug issues as they happen.
Automate agent refinement: Instead of manually digging through logs, Agent Optimizer automatically clusters real-world failures and suggests refined system instructions to improve accuracy.

Detailed technical guides and a full list of updates are available in our updated documentation and release notes. Agent Platform is the new standard for enterprise agent development, built to help you move from experimentation to production-scale impact, starting today.

Next ‘26: Redefining security for the AI era with Google Cloud and Wiz

Wed, 22 Apr 2026 12:00:00 +0000

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The AI era demands a new security era. Organizations are facing the dual challenge of harnessing the potential of AI while defending against its malicious use, and Google Cloud can help you adapt and thrive.

The latest research from Google Cloud shows that adversaries are using AI to accelerate the speed, scale, and sophistication of attacks. Meanwhile, M-Trends 2026 also showed that increased threat actor coordination has driven down the time to hand-off from an initial access to a secondary threat actor from eight hours to 22 seconds in the last three years.

Today at Google Cloud Next, we are showcasing how Google Cloud can help you defend against increasingly sophisticated threats at machine speed, protect AI and multicloud environments, and secure cloud workloads at scale.

Delivering agentic defense

Our full-stack AI approach, from the chips to the models, gives you a competitive advantage with better integration and velocity to help protect customers. Not only can Google action insights from the world’s largest threat observatory and Mandiant frontline experts, but we also bring cutting-edge insights and breakthroughs from Google DeepMind, to help make your platforms more secure.

Today we are introducing three new agents in Google Security Operations to help you defend at the speed of AI.

Threat Hunting agent, now in preview, can help teams proactively hunt for novel attack patterns and stealthy adversary behaviors that bypass traditional defenses.
Detection Engineering agent, now in preview, can identify coverage gaps and create new detections for threat scenarios, reducing toil and transforming detection creation from a manual craft into an automated science.
Third-Party Context agent, coming soon to preview, can enrich your workflows with contextual data from third-party content.

Initiating a threat hunt with the Threat Hunting agent

Our Triage and Investigation agent processed over 5 million alerts in the last year, reducing a typical 30-minute manual analysis to 60 seconds with Gemini.

“Operational resilience and cybersecurity are the bedrock of customer trust at BBVA. By integrating advanced artificial intelligence, such as the Triage and Investigation agent, we are able to scale in new ways," said Diego Martinez Blanco, head of Security Technology, BBVA.

“It handles the initial heavy lifting and filters out false positives so we can prioritize issues that require human attention. The agent's transparent explanations allow our team to understand recommendations and ultimately dedicate our resources to more complex investigations,” he said.

You can build your own security agents with remote Google Cloud model context protocol (MCP) server support for Google Security Operations, now generally available. To make it even easier, you can also access the MCP server client directly from the Google Security Operations chat interface, available in preview.

Organizations leveraging an intelligence-led, AI-augmented approach to modern security operations with Google Cloud's agentic defense can realize a strong ROI. Christopher Kissel
Research Vice President, IDC

Findings report created by the Threat Hunting agent

Security teams can also automate response actions with agentic automation in Google Security Operations. To further move teams from manual triage to agentic defense, we introduced dark web intelligence in Google Threat Intelligence, now in preview. Internal tests show it can analyze millions of daily external events with 98% accuracy to elevate threats that truly matter.

"IDC found that organizations experienced measurable operational gains, including substantial reductions in mean time to detect and mean time to respond, fewer false positives, and higher analyst productivity with AI-powered context and automation. These operational improvements translate into significant business outcomes, such as shorter disruption periods, lower incident-related costs, and improved executive confidence in security posture and decision-making," said Christopher Kissel, research vice president, IDC. "Organizations leveraging an intelligence-led, AI-augmented approach to modern security operations with Google Cloud's agentic defense can realize a strong ROI."

New partner-supported workflows for Google Security Operations

Today, we are also announcing a robust cohort of new partner integrations for Google Security Operations. Designed to deliver high-fidelity security workflows right out of the box, our latest participating Google Cloud Security integration ecosystem partners include Darktrace, Gigamon, and SAP.

Protecting AI and cloud applications across any infrastructure

AI and cloud applications are built across multiple platforms and models. To protect them end-to-end, we want to make it easier and faster to mitigate risk, regardless of where and how you build. This support includes major cloud environments like Amazon Web Services, Google Cloud, Microsoft Azure, and Oracle Cloud; software-as-a-service (SaaS) environments like OpenAI; and even custom hosted environments.

Wiz, now a part of Google Cloud, expands and deepens our ability to protect the apps you build and run. Wiz empowers you to quickly and securely adopt AI, while also helping protect the AI development lifecycle.

Wiz announced its AI-Application Protection Platform (AI-APP) at the RSA Conference, providing deep visibility, risk posture, and runtime analysis for your AI applications. Wiz also announced Wiz Security Agents and Wiz Workflows, helping you identify and respond to risks and threats at machine speed.

Today, we’re taking our commitment to secure customers in any cloud, platform, and AI environment further. Wiz now supports Databricks as well as new agent studios like AWS Agentcore, Gemini Enterprise Agent Platform, Microsoft Azure Copilot Studio, and Salesforce Agentforce, so customers gain visibility however their teams choose to build.

In addition, Wiz continues to support security ecosystems with integrations to the outer layer of the cloud, including Google Cloud Apigee, Cloudflare AI Security for Apps, and the Vercel platform, further extending the power of the Wiz Security Graph. We’ve also updated how we integrate security detections from Wiz Defend with Google Security Operations and Mandiant Threat Defense to help analysts more easily configure automatic threat information forwarding.

Wiz is also announcing new capabilities designed to secure the AI-native development lifecycle, helping teams to innovate faster and more securely:

Secure vibe-coded applications: Wiz is announcing a new integration, generally available in May, that runs Wiz security scanning directly inside the Lovable platform so vulnerabilities, secrets, and misconfigurations caught by Wiz surface in Lovable's built-in security view, right where teams are already building.
Secure AI-generated code: Wiz removes risks from AI-generated code the moment it is created. Inline AI security hooks integrate directly into IDEs and agent workflows to evaluate prompts and scan AI-generated output instantly, injecting security guardrails before the code is ever committed.
Agent-based remediation: Wiz Skills equip coding agents and AI-native IDEs with full code-to-cloud context and validated attack surface findings from the Wiz Security Graph. These capabilities enable teams to trigger automated, agent-driven remediation workflows either locally from the developer's individual IDE or globally at the repository and pull request level within your version control system.
Eliminate shadow AI: Wiz’s dynamic AI-Bill of Materials (AI-BOM) automatically inventories all AI frameworks, models, and IDE extensions across your environment. This provides complete visibility into what is writing code across your stack, allowing you to track sanctioned corporate tools like Gemini Code Assist and GitHub Copilot while simultaneously uncovering unapproved shadow AI plugins.

You can learn more about the Wiz announcements here.

Securing your agents and the agentic web

In addition to securing your cloud and AI workloads, Google Cloud’s secure-by-design foundation can help you innovate at the speed of AI — from agents to fraud defense to the web.

Securing and governing agents with the Gemini Enterprise Agent Platform
To build, orchestrate, govern, and optimize agents, today we are announcing Gemini Enterprise Agent Platform including:

Agent Identity to enable access management and AI governance at scale. Our new capability provides agents unique identities to operate autonomously with specific authentication flows, and with scoped human delegation.
Agent Gateway, which enables policy enforcement for all agent-to-agent and agent-to-tool connections. It governs your enterprise agent traffic and understands agent protocols like MCP and Agent2Agent (A2A) to inspect and secure every agent interaction.
Model Armor, our runtime protection for model and agent interactions, now integrates with Agent Gateway, Agent Runtime, and Langchain available in preview, and Firebase, generally available, to help developers add inline enforcement and sanitization of agent traffic and interactions without the need to change code. These integrations expand Model Armor's protection against runtime risks such as prompt injections, tool poisoning, and sensitive data leakage across Google Cloud services and our AI portfolio.

Securing the agentic web with Google Cloud Fraud Defense and Chrome Enterprise
Today, we are evolving reCAPTCHA with the launch of Google Cloud Fraud Defense, generally available. This comprehensive platform is designed to discern the legitimacy and authorization of bots, humans, and agents. Using the same scale and signals that protect Google’s own ecosystem, Fraud Defense will soon offer in preview agent-specific capabilities for human users and AI agents that can help secure the digital commerce journey, from account creation and login to payment and checkout.

Our commitment to securing AI extends to the browser, a vital endpoint for interacting with AI. Chrome Enterprise provides comprehensive data protection for the AI era with the visibility and controls needed to embrace AI safely without compromising corporate data:

AI-aware extension threat detections, now in preview, can surface advanced extension telemetry that helps security teams detect and respond to anomalous AI agent activity.
New shadow AI reporting, generally available soon, can help you gain visibility into the shadow AI landscape by flagging employee use of unsanctioned web-based AI and SaaS applications.

What’s new in Trusted Cloud

We continue to offer new security controls and enhance capabilities across identity, data, and networking on our cloud platform to help you secure your environments. Today we’re announcing the following updates:

Simplifying permissions with modern IAM
To help achieve least privilege quickly and simply, we’ve streamlined our predefined roles catalog with easy-to-use administrator, editor, and viewer roles, such as the IAM role picker and the ability to re-authenticate sensitive actions.

Data security
We are announcing several new capabilities for our cloud platform data security portfolio to help protect your most sensitive data and accelerate AI transformation.

Confidential Computing: In partnership with NVIDIA, today we’re announcing Confidential Computing support for G4 VMs, featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs on Google Compute Engine (GCE) Confidential G4 VMs, available in preview globally, to help strengthen confidentiality and integrity for a wide spectrum of sensitive AI workloads. In partnership with Intel, we’re also introducing the preview of C4 Confidential VMs, bringing Intel TDX to 6th Gen Xeon processors to help protect diverse AI and analytics workloads while providing industry-leading compute density and performance.
Cloud Key Management Services (KMS): We are announcing the new Confidential External Key Manager (cEKM) in preview, giving you the flexibility to host and protect external keys in any region and maintain verifiable control within a confidential environment.
Post-quantum cryptography (PQC): We are introducing KMS Quantum Safe Key Imports, available in preview, to help you bring your own keys with quantum-safe algorithms.
Secret Manager: To help prevent password leaks and mitigate prompt injection risks, we are announcing the general availability of the native integration of our Secret Manager with Agent Development Kit.

Network security
Google Cloud’s Cross-Cloud Network security products offer several new capabilities:

Cloud NGFW: We’re announcing the Cloud NGFW advanced malware sandbox, in preview later this year, to help defend against highly evasive zero-day threats. This capability is powered by Palo Alto Networks Advanced Wildfire, trained on data from more than 70,000 Palo Alto Networks customers to stop 99% of known and unknown malware.
Cloud Armor: We have released new Cloud Armor managed rules, powered by Thales Imperva and available in preview, to detect Layer 7 application attacks and zero-day CVEs (like React2Shell).

Advancing Google Cloud security with SCC
As our Google Cloud-native security solution, Security Command Center (SCC) establishes a cloud security baseline to protect both your traditional and AI applications on Google Cloud:

AI agents, models, and MCP servers are secured by providing continuous discovery and comprehensive risk analysis to identify threats, vulnerabilities, and misconfigurations.
SCC will add deep runtime visibility to uncover shadow AI for your Google Cloud workloads. Coming soon in preview, SCC will automatically discover unmanaged agentic workloads — including agents, MCP servers hosted on Cloud Run, GKE, and inference endpoints running on GKE, and surface those as posture findings in SCC.
Our enhanced Security Command Center Standard tier provides data security posture management, compliance, vulnerability management, and risk analysis to help any Google Cloud customer establish strong security, compliance and risk coverage from the start at no additional costs.

Take the next step

When you make Google part of your security team, you gain the power of an intelligence-driven, AI-native defense; the freedom of an open cloud that’s secure-by-design; and the industry's most-battle tested experts as an extension of your organization.

For more on these new innovations and how you can secure what’s next, tune in to watch our security spotlight. And be sure to check out the many great security breakout sessions — live and on-demand — to learn more about all of our Next ‘26 announcements.

The new Gemini Enterprise: one platform for agent development, orchestration, and governance

Wed, 22 Apr 2026 12:00:00 +0000

The first wave of AI changed how we find information; the next wave is changing how we get work done. Today, we’re enhancing our most powerful AI tools and bringing them together under one roof.

Gemini Enterprise is now an end-to-end system for the agentic era, built for agents that can execute complex, multi-step work processes. It combines access to frontier AI models, an intuitive UI, a secure development framework, and the ability to successfully deploy agents at scale.

At Google Cloud Next, we’re launching an expanded Gemini Enterprise portfolio that includes several key components:

Gemini Enterprise Agent Platform is our new developer platform and evolution of Vertex AI. It brings together our full suite of models, development, and tuning services, with new features for businesses to build, scale, govern, and optimize agents that can work autonomously to execute complex business workflows. Learn more here.
Gemini Enterprise app lets teams discover, create, share, and run AI agents in a single, secure environment. The Gemini Enterprise app is built on the Agent Platform, meaning all governance, security, and identity capabilities come standard, and it integrates with your enterprise data – including data in third-party systems–through connectors.
An open partner ecosystem to discover and deploy a wide range of third-party agents from leaders like Oracle, Salesforce, and ServiceNow – all within a secure and governed environment.

As organizations start building and managing thousands of agents, they need new ways to provide oversight and minimize sprawl. Gemini Enterprise enables IT departments to manage agent permissions and activities securely. It provides the same level of oversight and auditability found in essential business applications like payroll or quarterly financial reporting.

Gemini Enterprise’s unified approach provides multiple advantages across an organization:

For developers: Agent Platform is the foundation for the agentic enterprise built on our leading infrastructure and deeply integrated with our data and security capabilities. It’s where technical teams can build innovative agents that transform your products and services, alongside agents that scale your internal operations. With tools like low-code Agent Studio, support for Agent Development Kit (ADK), and protocols like A2A and MCP, Agent Platform is the key to building, scaling and optimizing agents across the entire organization.
For IT teams: We provide a single control plane for governance in Agent Platform, so every employee can use and share agents with full IT visibility. Both no-code and pro-code agents are managed through a consistent model for identity, security, and auditing – preventing AI sprawl without slowing down innovation.
For knowledge workers: Gemini Enterprise app is the front door for AI for every employee in your organization. Knowledge workers can build their own agents with our no-code Agent Designer or leverage a wide range of partner-built agents from Google Cloud Marketplace. And these agents can be easily shared across the organization with full governance and compliance controls built-in. Inbox in Gemini Enterprise provides a central location to manage and monitor agents, including long-running agents.

Gemini Enterprise Agent Platform: securely orchestrating the agentic enterprise

At the heart of the Gemini Enterprise experience is our new Agent Platform. It’s the central place where developers can build, scale, govern, and optimize fleets of agents. Today, we're introducing new capabilities across four key areas:

Build: An enhanced Agent Development Kit (ADK) helps you unlock more powerful reasoning by organizing agents in a network of sub-agents. With ADK’s new graph-based framework, you can orchestrate complex processes with new ecosystem integrations and support for MCP.
Scale: With our improved Agent Runtime, your agents can be both fast and persistent, and with agent-to-agent orchestration, they can seamlessly delegate tasks to one another. The launch of Memory Bank and Memory Profiles also ensure agents operate with long-term contextual memory, allowing them to recall user preferences and past history across different sessions.
Govern: Our secure-by-design architecture gives IT teams centralized control over their entire agent lifecycle. Agent Identity assigns every agent a unique cryptographic ID for complete traceability and auditing, while Agent Gateway provides air traffic control to ensure interactions between agents and data is secure. Model Armor then protects against activities like prompt injection, tool poisoning, and sensitive data leakage.
Optimize: To move beyond trial-and-error, the new Agent Simulation tool allows you to stress-test your agents against real-world scenarios before they ship. Agent Observability also helps ensure agents function exactly as intended, providing a real-time view into safety and performance.

Any agent built on Agent Platform can then be instantly surfaced in the Gemini Enterprise app for your employees to access. By building on a foundation where security and identity are included by default, you can stop worrying about managing risk and start focusing on managing agent-led business outcomes.

Learn more: What's new in Agent Platform

The Gemini Enterprise app: your hub for everyday work

In the Gemini Enterprise app, teams can discover, create, share, collaborate, and run agents in a single environment. With data governance and compliance built in, it gives your teams a safe place to leverage agents grounded with your business data. It also provides a simple interface for users to engage, and democratizes the power of agent creation, orchestration, and scaling to every individual in the organization.

New capabilities available in the Gemini Enterprise app include the ability to:

Innovate with no-code agents: Using the newly-integrated Agent Designer, anyone can create agents to automate complex, multi-system processes with simple, natural language. This includes schedule or trigger-based agents, including those triggered by another business application, even via a partner connector.
Centralize agent oversight: Inbox in Gemini Enterprise provides a unified hub for managing agents at scale, including long-running agents executing complex workflows. Users can monitor active agents, adjust configurations, and receive real-time status alerts directly through email and chat, ensuring automated processes stay on track.
Establish persistent team memory: Moving beyond individual chats, Projects creates a shared workspace for teams and agents. It centralizes the context and history of a given topic, ensuring that critical project intelligence remains a permanent company asset even as team members or roles change.
Collaborate in a dynamic workspace: Canvas provides teams with an interactive editor to co-create and edit in Google Docs and Slides. We’ve also added Microsoft 365 interoperability, allowing you to export the Docs and Slides you create within Canvas into common Microsoft Office formats.

Learn more: What's new in Gemini Enterprise

The agent economy: amplified by our partner ecosystem

No organization can build every agent they need from scratch. The agentic enterprise thrives on an open, interoperable ecosystem. That's why we are helping to make it easier than ever to discover and deploy specialized agents from our partners.

Benefits include:

Activating third-party agents from within the Gemini Enterprise app: We are now bringing the full catalog of partner-built agents from Google Cloud Marketplace directly into the Agent Gallery. You no longer need to leave your workflow to find and deploy powerful third-party agents. The app provides a secure request-and-approval gateway that connects employee needs to IT-validated solutions.
A growing library of trusted solutions: Agent Gallery features an expansive collection of validated agents from SaaS leaders and innovative startups. For example, you can use specialized agents from partners like Adobe, Salesforce, ServiceNow, and Workday–directly within the Gemini Enterprise app.
Deploy with confidence: Every partner agent in the gallery has been validated by Google Cloud to meet our requirements for security and interoperability. This allows you to benefit from a world of innovation, while maintaining the centralized governance and control your business demands.

Learn more: Partner-built agents available in Gemini Enterprise

Gemini Enterprise brings the best of Google AI to every employee, every developer, and every customer — empowering them with cutting-edge AI agents. Get started with Gemini Enterprise today.

Gemini Enterprise for the agentic task force: introducing long-running agents, agentic collaboration spaces, advanced governance, and more

Wed, 22 Apr 2026 12:00:00 +0000

As we have deployed Gemini Enterprise across various industries over the last few months — including the world's most complex and heavily regulated organizations — a clear shift has emerged. Enterprises don't just need AI to answer questions or complete isolated tasks. They also need autonomous agents that can execute complex, multi-step workflows, capable of running for hours or days.

Companies are ready to build their agentic task force, but this demands doing so within a secure and governed environment. This includes creating and deploying agents with their own identity, registry, and gateway so they can always be traced, monitored, and managed.

Today, we are expanding Gemini Enterprise to meet this demand, transitioning AI from an isolated productivity tool into a secure, collaborative, autonomous engine for your business. Gemini Enterprise is the end-to-end system for the agentic era, including both the Gemini Enterprise app and our newly announced Gemini Enterprise Agent Platform.

At Google Cloud Next, we are announcing several new capabilities in the Gemini Enterprise app, including the ability to:

Combine generative intelligence and deterministic business logic to manage enterprise reliability: Build agents ranging from simple assistants to highly complex autonomous orchestrators—without writing a single line of code. And, using our enhanced Agent Designer and reusable Skills, you can construct these workflows entirely with natural language or through a visual interface.
Scale the agentic task force with autonomy: Build and deploy long-running agents that independently solve complex problems up to days at a time, managed entirely through a new, unified Inbox command center.
Collaborate with your colleagues and agentic task force in real-time: Transition from individual AI to collaborative, team-level productivity with Projects — a shared workspace for teams and agents—and the integrated Canvas editor.
Govern with confidence with complete agent observability and traceability: The Gemini Enterprise app is built on our new Agent Platform, meaning enterprise-grade governance, observability, and granular control are built-in from day one.
Connect your ecosystem: Orchestrate actions across Google Workspace, Microsoft 365, and a growing ecosystem of partner agents and data connectors.

Automate complex work with a digital taskforce

With our suite of agent building and management tools, anyone can manage a full spectrum of work — from immediate, ad-hoc tasks, to asynchronous, multi-step workflows that don’t require constant manual prompting.

Enhanced Agent Designer: Create an agent using natural language or a visual interface. Balance generative intelligence with deterministic nodes and flows that carry your business logic reliably. You can inspect, test, and approve every step of a workflow before it ever runs, ensuring enterprise transparency and trust. You can also seamlessly integrate human-in-the-loop checkpoints — ensuring your team can review actions, grant approvals, or provide expert guidance exactly when it matters most. These agents don’t just execute; they learn and improve with every human interaction.

Skills for reusable actions: Codify your unique expertise into reusable Skills. Skills let you formalize a specific workflow — like applying brand guidelines or formatting a report — and save it as a reusable action for your whole team. Instead of re-explaining the context every time, the agent uses the appropriate skill automatically. Because our models dynamically load these Skills, your agents remain incredibly cost-efficient and laser-focused. They pull in deep expertise only when the task requires it, without cluttering the model’s reasoning.

Long-running agents: Build and direct agents that can handle massive, multi-step workflows like end-to-end financial reconciliation or deep sales prospect sequencing, from ad hoc to recurring tasks. Operating autonomously from hours to days inside Google's secure cloud sandboxes, these agents orchestrate complex business logic in the background — powering through mission-critical work without requiring constant human supervision.

Inbox for agent management: Inbox is a central command location to monitor, guide, and securely manage all of your agent activity. Notifications are intuitively categorized into actionable groups like "Needs your input," "Errors," and "Completed," giving you a clear, consolidated view of ongoing agent progress.

Dynamic and interactive Agent UI experiences: Move beyond simple text chat. With new support for the Agent-to-UI protocol (A2UI), custom agents can dynamically generate rich, native UI components — like interactive data visualizations and structured forms — directly within the Gemini Enterprise app.

High-code agents: Allow your engineering teams and IT leaders to build sophisticated agents with full code and publish them directly into the Gemini Enterprise app for the business to use.
Data Insights agent for unified analysis: Data Insights is a native, out-of-the-box agent built by Google that uncovers deep patterns across your entire data landscape. By bridging structured data in warehouses and databases with the rich context of unstructured sources like docs, email, and chat, it provides a 360-degree view of your operations. The agent dynamically generates SQL and search queries to produce rich data visualizations — answering not just the “what,” but the critical “why” behind your business outcomes.

Enhanced Deep Research agent: Execute complex, multi-step research plans that autonomously synthesize information from the open web and internal company data. By combining iterative reasoning with long-running inference, the Deep Research agent can work in the background for hours to deliver comprehensive, citation-backed reports and executive summaries.

Team productivity: Projects and Canvas

Real work is a team sport. We are moving AI from a solo conversation to a persistent, shared team asset.

Projects: A dynamic, highly collaborative workspace where human experts and agents actively co-create side-by-side. By unifying your shared context across various sources — Google Workspace, Microsoft OneDrive, team chats, and more — the agent acts as a dedicated expert on your specific initiative. Whether your team is collaborating in real time or catching up asynchronously, you can work together to brainstorm new ideas, co-create key artifacts and make informed decisions that ultimately drive impactful outcomes, without ever having to sift through fragmented context.

Canvas: Stop tab-switching with an interactive editor built directly into Gemini Enterprise. Canvas allows teams to create and edit Docs or Slides in a single pane, with rich formatting and live co-editing. We’ve also added Microsoft 365 interoperability, allowing you to export the docs and slides you've created within Canvas into common Microsoft Office formats.

An open and connected enterprise ecosystem

The enhanced Agent Gallery: Your Agent Gallery is the unified destination for every agent your teams need — whether built by Google, created internally by your company, or discovered through our partners. Today, we are launching Agent Marketplace integrated into the Agent Gallery, giving your users the ability to easily browse and request specialized third-party agents from partners like Accenture, Oracle, and ServiceNow. Once approved, these agents seamlessly appear in the Agent Gallery, giving your team access to execute complex workflows with natural language right from within Gemini Enterprise.

Integrations for Bring Your Own Model Context Protocol (BYO-MCP): In addition to a rich set of rigorously validated, out-of-the-box connectors, we are introducing support for BYO-MCP. This allows admins to securely connect the Gemini Enterprise app directly to your own custom or third-party business tools, internal workflows, and custom-built applications. This capability expands your automation potential by allowing no-code agents to discover and execute tools hosted on your own servers — tailoring the AI experience to your specific enterprise workflows.
Cross-platform unified intelligence: Gemini Enterprise delivers unified intelligence across your entire fragmented data landscape. Whether your data lives in Google Workspace, Microsoft 365, or other business applications, Gemini Enterprise strictly respects native permissions — ensuring your teams get powerful insights without ever compromising your company's governance or user access controls.

Enterprise-scale governance comes standard without additional cost

Confidence in enterprise AI is built on layers of trust. The Gemini Enterprise portfolio provides it at every level – without added cost. The entire system is built on Google’s secure-by-design infrastructure, with Agent Platform at its core providing access to universal governance controls. Any agent built on both Agent Platform and the Gemini Enterprise app is governed by the same universal rules, giving businesses a deeply integrated way to help secure agentic work.

Key controls now include:

Enforce granular access controls: Agent Identity lets you assign unique digital IDs and help enforce the principle of least privilege for every agent no matter where it runs.
Organize and share approved agents: Agent Registry gives IT teams a central catalog to curate and distribute approved, reusable agents for their employees.
Turn “Shadow AI” into “Managed AI”: Agent Gateway gives IT teams a centralized management point for network policies, data access, and security guardrails — protecting against risks like prompt injection. Powered by our Context Aware Access capabilities, the Gateway enforces your company-specific rules and ensures agents never send data to unapproved endpoints.

Learn more about Gemini Enterprise Agent Platform here.

Availability

New features will roll out to customers over the coming months. Ready to explore these new features and empower your teams? Get started with Gemini Enterprise today.

What’s next in Google AI infrastructure: Scaling for the agentic era

Wed, 22 Apr 2026 12:00:00 +0000

AI is evolving from answering questions to reasoning and taking action. Companies who want to lead in today’s agentic era require computing infrastructure designed and optimized for these new requirements. Today at Google Cloud Next, we are introducing new AI infrastructure capabilities that help you innovate faster, deliver compelling user and customer experiences, and optimize for cost and energy efficiency — all at massive scale.

The shift to agentic intelligence

In the agentic era, a single intent triggers a chain reaction. Unlike chat, a primary AI agent decomposes goals into specific tasks for a fleet of specialized agents that then collaborate, preserve state, and use reinforcement learning to deliver outcomes in real-time.

This process scales intelligence per interaction, but also creates complexity that yesterday’s architectures cannot support without spiraling costs or performance bottlenecks. To scale efficiently and effectively, you must move beyond manually integrating fragmented components and technologies. To deliver agentic experiences that are smart, fast, scalable, and cost-effective, you need a unified infrastructure stack that spans purpose-built hardware, open software, and flexible consumption models.

Google’s AI Hypercomputer is AI-optimized infrastructure built for the agentic era, engineered to deliver on these new requirements. This is the same foundation that powers Google’s flagship Gemini models, consumer AI services, and enterprise AI offerings. Today, we are announcing a significant expansion of our AI infrastructure portfolio, including:

TPU 8t and TPU 8i, our eighth generation TPUs
A5X bare metal instances, powered by NVIDIA Vera Rubin NVL72
Axion N4A VMs, powered by our custom Axion Arm-based CPUs
Google Compute Engine 4th generation VMs, powered by Intel and AMD x86-based CPUs
Virgo Network, our breakthrough data center fabric for AI workloads
Google Cloud Managed Lustre, a high-performance parallel file system
Z4M VMs with high-capacity local SSD storage and RDMA for open parallel file systems
Dedicated KV Cache scalable storage subsystem
Native PyTorch support for TPUs
New Google Kubernetes Engine (GKE) capabilities for agent-native workload orchestration

Taken together, these capabilities will help you accelerate the development of models and complex agentic workflows to accelerate innovation, and deliver useful, responsive services to customers, all while reducing costs and using energy responsibly at scale.

Let’s take a closer look.

Introducing our eighth-generation TPU systems, purpose-built for agentic AI

Today, we’re pleased to announce the eighth generation of our Tensor Processing Units (TPUs), which for the first time includes two distinct chips and specialized systems, engineered specifically for the agentic era.

TPU 8t is our training powerhouse, specifically designed for high-throughput AI workloads. It redefines the scale of AI development, delivering nearly 3x higher compute performance than previous generations to shrink training timelines for massive models. It packs 9,600 chips in a single superpod to provide 121 exaflops of compute and two petabytes of shared memory connected through high-speed inter-chip interconnects (ICI). This massive pool of compute, unified memory, and doubled ICI bandwidth helps ensure that even the most complex models achieve near-linear scaling and maximum system utilization. We can now turn months of training into weeks with the power of 1 million+ TPU chips in a single cluster, orchestrated by Pathways and JAX.

TPU 8i is our breakthrough reasoning system for inference and reinforcement learning (RL), engineered to deliver the ultra-low latency required by agentic workflows and Mixture of Experts (MoE) models. By tripling on-chip SRAM to 384 MB and increasing high-bandwidth memory (HBM) to 288 GB, it breaks the memory wall, hosting massive KV Caches entirely on silicon. Additionally, it doubles ICI bandwidth to 19.2 Tb/s, reduces the ICI network diameter by over 50%, and introduces a dedicated Collectives Acceleration Engine (CAE), which reduces on-chip latency by up to 5x to minimize lag during high-concurrency requests. With this design, TPU 8i delivers 80% better performance per dollar for inference than the prior generation, enabling fast, interactive user experiences, cost-effectively.

TPU 8t and TPU 8i will be available to Cloud customers soon. To learn more, check out this deep dive on the architecture.

A5X with NVIDIA Vera Rubin platform

We know that one size doesn't fit all. Different customers have different workloads, different requirements, and different use cases. So, we also partner deeply with NVIDIA to deliver the latest GPU platforms as highly reliable and scalable services in Google Cloud. We will be among the first to deliver instances based on the next-generation Vera Rubin platform when it becomes available later this year.

We are also co-engineering the open-source Falcon networking protocol with NVIDIA via the Open Compute Project, pushing the frontiers of reliable transport protocols. A5X will implement a variety of innovative concepts from Falcon.

Thinking Machine Labs, for example, uses our NVIDIA-based infrastructure to power Tinker, an open platform for reinforcement learning and fine-tuning of frontier models for specialized use cases, achieving over 2x faster training and serving with Google’s AI Hypercomputer.

Fueling agentic logic and reinforcement learning with Axion, Intel, and AMD

While GPUs and TPUs are great for training and serving AI models, they need to be complemented with high-performance CPU-based services to handle the complex logic, tool-calls, and feedback loops that surround the core AI model. Our new Axion-powered N4A CPU instances deliver outstanding price-performance for these agent runtimes. In fact, GKE Agent Sandbox with Google Axion N4A offers up to 30% better price-performance than agent workloads on other hyperscalers. This efficiency extends across our entire portfolio, including our 4th generation Compute Engine VM families, powered by the latest x86 instances from Intel and AMD. These are specifically optimized for the broadest range of RL tasks, such as RL reward calculation, agent orchestration, and nested visualization, providing the optimal capabilities for every AI workload.

Virgo Network for data center scale-out fabric

As part of AI Hypercomputer, the Virgo Network is designed to meet the demanding requirements of modern large-scale AI workloads. Its collapsed fabric architecture with 4x the bandwidth of previous generations eliminates the "scaling tax" to deliver staggering peak computing power. This capacity helps the most ambitious AI workloads scale with near-linear efficiency.

With Virgo Network and TPU 8t, we can connect 134,000 TPUs into a single fabric in a single data center, and connect more than one million TPUs across multiple data center sites into a training cluster — essentially transforming globally distributed infrastructure into one seamless supercomputer.

We are also making Virgo Network available for A5X (powered by NVIDIA Vera Rubin NVL72), supporting up to 80,000 GPUs in a single data center, and up to 960,000 GPUs across multiple sites.

Storage: Minimizing data bottlenecks

A massive compute cluster is only as effective as the storage system feeding it data. To ensure storage is not a bottleneck while making compute faster, we are delivering four key storage advancements that let you:

Accelerate training and inference: Google Cloud Managed Lustre now delivers 10 TB/s of bandwidth — a 10x improvement over last year and up to 20x faster than other hyperscalers. We’ve also increased its capacity to 80 petabytes. These advancements are powered by our new C4NX instances and Hyperdisk Exapools.
Minimize latency: Managed Lustre can leverage new TPUDirect and RDMA to allow data to bypass the host, moving directly to the accelerators. By removing this processing overhead, your AI agents can respond with the near-instant speed users need.
Maintain peak utilization for training: Rapid Buckets on Google Cloud Storage transforms object storage with sub-millisecond latency and 20 million operations per second. This helps ensure large-scale training checkpoints and recoveries happen near-instantly, allowing your accelerators to maintain 95% utilization or higher, accelerating training cycles, while also providing cost-effective utilization of valuable TPUs and GPUs.
Build custom solutions: For ISVs and organizations that want to build storage solutions, we are launching the Z4M instance, specifically engineered for customers who want to integrate trusted parallel file systems like Vast Data or Sycomp. Each Z4M instance scales to a massive 168 TiB of local SSD capacity and can be deployed in RDMA clusters of thousands of machines.

These new storage options provide a comprehensive storage portfolio, giving you the raw power of the AI Hypercomputer stack with optimal storage services for each use-case.

GKE: Orchestration for agent-native workloads

In the agentic era, intelligence is only as effective as the speed at which it can be scaled. So, we’ve transformed GKE to serve as the premier orchestration engine for agent-native workloads.

Reducing latency across the stack
To support responsive agentic responses, we optimize every millisecond of the start-up and scale-out process. By streamlining how infrastructure responds to surges in demand, GKE ensures that your agents are ready the moment a user engages with the system. New in GKE are:

Accelerated node and pod startup: GKE nodes now start up to 4x faster, while pod startup times have been slashed by up to 80%.
Rapid model loading: Leveraging the run:AI Model Streamer and Rapid Cache in Google Cloud Storage, models now load 5x faster, removing a traditional storage bottleneck.

Intelligent routing with AI-powered Inference Gateway
Building on last year's introduction of GKE Inference Gateway, we are using "AI for AI" to solve the complexities of serving at scale.

Inference Gateway’s new predictive latency boost replaces heuristic guesswork with machine learning-driven, real-time capacity-aware routing. This intelligent orchestration cuts time-to-first-token (TTFT) latency by more than 70% without manual tuning. For businesses, this translates directly into more natural voice conversations and smooth, real-time interactions across a range of use cases.

Inference Gateway can be deployed alongside llm-d, a Kubernetes-native high-performance distributed LLM inference framework, which was recently accepted as a Cloud Native Computing Foundation (CNCF) Sandbox project. Google Cloud is proud to be a founding contributor to llm-d alongside Red Hat, IBM Research, CoreWeave, and NVIDIA, uniting around a clear, industry-defining vision: any model, any accelerator, any cloud.

Open software ecosystem for the full AI lifecycle

Hardware reaches its full potential through co-designed software. AI Hypercomputer enables engineers to move faster by providing native, optimized support for the industry’s most popular frameworks, including JAX, PyTorch, and vLLM. This open software layer reduces friction between development and deployment, translating to faster time-to-market and better resource efficiency.

We are now in preview with select customers with native PyTorch support for TPU, which we call TorchTPU. With TorchTPU, you can run models on TPUs as they are, with full support for native PyTorch features like Eager Mode. When you combine this with our robust support of vLLM on TPU, our message is clear: we always focus on building for openness and customer choice.

Your foundation for agentic growth

To innovate quickly and cost-effectively in the agentic era, you need a unified system that doesn’t compromise on performance or choice. That is exactly what AI Hypercomputer delivers. By co-designing every layer — from the silicon to the software — we remove the integration burden so your teams can focus on driving your business forward.

AI Hypercomputer also serves as the powerful foundation for Google’s entire ecosystem of high-level services. This integrated stack powers everything from Gemini Enterprise to the Gemini Enterprise Agent Platform, ensuring that all these infrastructure innovations translate directly into business value. By leveraging our fully managed services, such as our serverless training service and our new Managed RL API, you can apply AI Hypercomputer’s massive performance gains to customize Gemini with your own business logic, delivering sophisticated, agent-based solutions.

We’re looking forward to seeing what you build next with this updated and expanded AI platform.

Google Cloud and SAP unveil blueprint for the Agentic Enterprise

Wed, 22 Apr 2026 12:00:00 +0000

Google Cloud and SAP are deepening their partnership, working together to embed Gemini AI directly into the core business processes of the world's largest enterprises.

A new foundation for AI: Today at Google Cloud Next '26, we are joining SAP to showcase the latest breakthroughs for the Agentic Enterprise from our ongoing partnership.

These include the Unified Data Foundation, which integrates SAP and non-SAP data into a bi-directional source of truth, transforming routine cloud modernization into a strategic engine for data-driven value. Our new zero-copy data sharing capabilities further simplify architectures by removing the friction of data movement, ensuring the high-fidelity data access and real-time reliability essential for building mission-critical AI workloads.

We’re also supporting new capabilities in the SAP Engagement Cloud to deliver the latest in AI creation, ideation, and learning tools. These platforms are available not only to SAP employees but also as an offering for SAP’s own customers. This suite of AI tools will help drive agentic understanding and adoption across the enterprise.

Why it matters: This shift closes the gap between data storage and AI innovation. It empowers organizations to move from simply managing infrastructure and start deploying intelligent agents that can autonomously execute complex, multi-step tasks.

The Unified Data Foundation in particular offers not only valuable insights for existing workflows but also helps power the outputs of AI tools and agentic workflows.

The big picture: The deepening partnership is already transforming how SAP delivers value to its own customers.

Driven by an internal mandate to maintain market relevance and build a competitive advantage, SAP's Engagement Cloud division partnered with Google Cloud to build next-generation agentic solutions. These include agents for dynamic content development, generation of marketing briefs and visual concepts, and collaborative multi-agent execution.

What they're saying: “Agentic AI only creates value when it’s grounded in trusted data and directly connected to execution, which is what makes our partnership with Google Cloud so exciting for businesses. By embedding generative and agentic intelligence directly in Engagement Cloud, Google and SAP are giving marketing teams a practical way to turn real-time intelligence into meaningful customer interactions.” – Joanna Milliken, Head of Engagement Cloud, SAP

What's new:

The zero-copy revolution: SAP BDC Connect for BigQuery delivers bidirectional, zero-copy data sharing. You can unify your entire data footprint without moving or duplicating massive datasets.
Cortex Framework: New solution accelerators embed rich metadata directly into BigQuery. This grounds Gemini agents in an accurate enterprise context, heavily mitigating AI hallucination risks.
Elevated 99.95% SLA: An industry-leading uptime guarantee for RISE with SAP on Google Cloud, driven by AI-powered predictive outage prevention.
SAP Sovereign on Google Cloud: Customers with strict data residency mandates can now run SAP's S/4HANA Private Cloud Edition directly on Google's sovereign infrastructure.

By the numbers: Customers can achieve up to a 54% lower total cost of ownership (TCO) for data analytics. Hosting SAP workloads on Google's energy-efficient public cloud also instantly accelerates corporate ESG targets.

What's next: SAP BDC Connect for BigQuery will soon be available in select data centers, with broader availability in the second half of 2026.

Go deeper:

Startups are building the next big thing with Google Cloud AI

Wed, 22 Apr 2026 12:00:00 +0000

The future is taking shape in Las Vegas this week, where the world’s leading startups are showcasing their groundbreaking AI work at Google Cloud Next. Whether they need the top AI models, infrastructure purpose-built for the most demanding workloads, intelligent multi-model databases, or security and sovereignty solutions to grow with confidence — or all of the above — startups of all sizes and in every industry are choosing Google Cloud.

To take one example of thousands, Lovable, the popular software creation platform, utilizes Google Cloud infrastructure and builders are now creating over 200,000 new projects on Lovable every day. OpenEvidence, a medical AI platform widely used by U.S. physicians, uses Google's AI tools to provide evidence-based answers at the point of care; the company recently announced major partnerships with Wiley and Mount Sinai, expanding access to trusted medical research and to its AI assistant.

This week at Next we’re announcing even more startups who are starting or expanding their work with Google Cloud. These include leading names in areas like vibe coding, model training and inference; healthcare and life sciences; image generation; financial services; gaming and much more. It also includes an exciting new group of startups who have recently begun accessing GPU-based AI compute through Google Cloud’s AI Hypercomputer, thanks to our deep technical partnership with NVIDIA.

Google Cloud has always offered startups the entire AI stack in one unified place, giving founders maximum velocity. With new exciting launches at Next ‘26, the options and opportunities are only growing.

We’re helping you build complex agents faster with Gemini Enterprise Agent Platform, optimize compute needs with two differentiated TPU offerings for inference and training, reduce data friction with our next-gen cross-cloud Lakehouse, and better govern everything with the help of Wiz AI agents and Google-scale threat intelligence, to name just a few advances. We’ve also unveiled the ability to sell your AI solutions directly to enterprise users through marketplace integrations in Gemini Enterprise.

Supporting startup innovation across categories

If there was any question of just how dramatically AI is transforming every facet of work and life, look no further than the innovations being brought to market by our startup customers.

Coding and developer tools

Emergent AI uses Google Kubernetes Engine (GKE) as its core infrastructure to orchestrate thousands of on-demand, isolated development sandboxes for vibe coding. By leveraging GKE Autopilot for automated scaling and Gemini models for code generation, the platform transforms natural language prompts into live, production-ready applications with instant previews.
Lovable is expanding its work with Google Cloud and launching a new coding agent, now available to purchase through Google Cloud Marketplace and Gemini Enterprise.
Parallel builds high accuracy web search and research APIs, purpose-built for AI agents. Leveraging Google Cloud offerings including Gemini, BigQuery, BigTable, and Spanner has enabled them to build, launch and massively scale their platform since launching in August 2025.
Temporal partnered with Google Cloud to build a unique Application Development Kit (ADK) integration. This enables organizations to deploy agentic systems for mission-critical use cases that require strong execution guarantees, uptime, auditability, and operational resilience.

Customer engagement

Satisfi Labs is an agentic AI platform designed to transform customer engagement for destinations and experiences, serving 800 clients across sports, entertainment, and tourism. They are built on the Google Cloud ecosystem and leverage technologies including Gemini, BigQuery, AlloyDB, and Datastream to enable intelligent interactions, scalable data processing, and real-time insights.
Vapi, a platform for developers creating conversational voice AI, leverages Gemini as its intelligence core, allowing developers to maximize their compute-per-dollar while delivering a premium voice experience with incredible scale.
Vurvey Labs leverages Gemini Enterprise Agent Platform to build agent populations and simulated worlds that predict human behavior and future needs.
ZenBusiness utilizes Gemini Enterprise Agent Platform to simplify business ownership, including the launch of 'Velo,' an AI assistant for formation, compliance, and website building.

Document intelligence

Reducto leverages Gemini Enterprise Agent Platform and AI Studio alongside its core models to automate high-stakes workflows and power production AI pipelines at scale.

Finops

Mavvrik is a FinOps platform using Gemini and BigQuery to power their unified cost management platform for FinOps, IT, and finance leaders to gain visibility, automate allocation, and govern spending across public clouds, private clouds, AI workloads, and SaaS applications.

Generative media

ComfyUI is streamlining complex node-based gen media production by integrating Nano Banana Pro while improving the consistency of their customer experience using Provisioned Throughput.
Gamma used by over 70 million people to create presentations, documents, websites, and social posts, uses Nano Banana 2 to instantly generate localized visuals and transform ideas into ready-to-share content in seconds.

Industry AI

Chorus, spun out of Alphabet’s X innovation arm, is building AI orchestration tools to transform how physical assets are made, moved, and managed across industries. Leveraging Gemini Enterprise Agent Platform, advanced machine learning, and proprietary algorithms, Chorus provides granular asset-level visibility, temperature intelligence, and dynamic delivery estimates across mission-critical supply chains.
ExaCare AI, an AI platform for post-acute care operations, is using Gemini Enterprise Agent Platform and Gemini for inference and to process clinical and operational data across admissions and reimbursement workflows.
Industrility, an AI-powered aftersales platform for machine manufacturers, built a maintenance intelligence engine on Google Gemini’s advanced language models, extracting critical maintenance tasks and reducing a two-month manual project to three hours.
Insilica uses Gemini Enterprise Agent Platform to process millions of toxicology regulatory documents and thousands of databases into a unified, agentic risk assessment platform.
Proximal Health utilizes DocAI to automate the insurance claims adjudication process.
Stord, which offers fulfillment and software for omnichannel retail, leverages Gemini Enterprise Agent Platform to build ML models that predict estimated delivery dates and optimize demand planning across millions of orders, all grounded in real data behind over $10B in annual Gross Merchandise Value.
Stylitics uses Gemini Enterprise Agent Platform to power more than 1,000 AI programs for world-class retailers, including AI image generation, outfitting and styling, and AI analytics.
Wand uses Gemini Enterprise Agent Platform to power Game Guide, a contextual companion built into their single-player PC gaming platform, that helps players get unstuck, uncover hidden content, and stay immersed without ever leaving the game window.

Productivity

Notion is at the forefront of integrating artificial intelligence into productivity tools, leveraging Gemini models to power its text and image generation capabilities.

Sustainability

Watershed, a leading enterprise climate platform, is leveraging Google Cloud to transform sustainability data into actionable climate intelligence, accelerating global decarbonization and enabling organizations to move from measurement to action at an unprecedented scale.

Supporting model training and inference with NVIDIA AI infrastructure

Today, when the world’s most innovative startups need to access NVIDIA’s cutting-edge GPU-powered compute, many of them rely on Google Cloud. Our longstanding partnership with NVIDIA allows these startups to train and serve AI models, support fine-tuning, and scale new agentic workloads.

This week at Google Cloud Next, we’re highlighting the successes these customers are having and announcing exciting new partnerships with more startups choosing to utilize NVIDIA AI infrastructure and Google Cloud’s AI stack.

These new and expanding customers include:

Thinking Machines Labs will utilize the latest NVIDIA Blackwell chips through Google Cloud, including being among the first Google Cloud customers to utilize GB300s. In early testing, Thinking Machines has seen training and serving speed increase 2x on GBs compared to prior generation chips.
GMI Cloud leverages Google Cloud’s GPU infrastructure to run its inference-optimized platform, powering GMI Studio workflows and large-scale model serving across text, image, and video. This infrastructure supports high utilization driven by growing demand for real-time, production-grade AI applications.
Inferact is developing vLLM, a highly performant and easy to use inference engine for open source LLMs on data center hardwares, utilizing a cluster of NVIDIA GB200’s on Google Cloud, as well as services like Managed Lustre and Cloud Filestore.
Neuraldefend is partnering with Google Cloud and NVIDIA to utilize H100 and L4 GPUs for model training and production inference for their real-time deepfake detection engine and digital trust solutions.
Nucleus AI is utilizing a cluster of NVIDIA H100 GPUs through Google Cloud for model training.
Reflection has expanded its usage of G4 VMs through Google Cloud, as well as services like Google Cloud Storage to support training and inference for its open-weight models.
Reve uses A3 Mega (NVIDIA H100 GPUs) and A3 Ultra (NVIDIA H200 GPUs) instances on Google Cloud to handle their intensive multimodal foundation model training and inference. They also use GKE to manage their high performance training needs.

Startups most commonly access NVIDIA GPUs through Google Cloud G4 VMs, powered by NVIDIA’s latest Blackwell infrastructure. We’re also excited to offer NVIDIA’s Vera Rubin architecture to startups later this year. Our deep partnership with NVIDIA means that the Google Cloud AI Hypercomputer is highly optimized for GPU customers and can deliver ultra-low latency, high-throughput, and cost-effective performance across multiple use cases.

Startups are choosing to utilize GPUs through Google Cloud in part because of this high level of optimization and because they can easily access highly differentiated technologies in our AI stack like Gemini Enterprise, Agent Builder, BigQuery, Google Kubernetes Engine, and much more.

We also continue to partner closely with NVIDIA to support early-stage startups with AI compute and cloud services through NVIDIA’s Inception program and our Google for Startups Cloud Program. This includes exciting startups like:

Aible, which is building long‑running enterprise AI agents. They are accessing GPUs on Google Cloud to help them run Nemotron 3 and NemoClaw agents directly on BigQuery, orchestrate multi‑agent blueprints like the Intelligent Warehouse, and deploy serverless RTX‑accelerated inference on Cloud Run.
Baseten provides a managed inference platform that optimizes model serving for large language and vision models. They are accessing GPUs on Google Cloud to help them deploy NVIDIA‑accelerated inference with improved tokens‑per‑second, cost‑per‑1K requests, and GPU utilization for customers running production gen AI workloads.
CodeRabbit builds AI-driven workflows for code review, supported by Nemotron models via Google Enterprise Agent Platform, delivering low-latency PR summarization, embedding-powered context enrichment, and scalable concurrent review pipelines.
Factory AI develops autonomous code-editing agents that can traverse complex code graphs, resolve dependencies, and generate safe patches against real production repositories. Nemotron models in Gemini Enterprise Agent Platform allow Factory AI to power Nemotron Super-driven "droids" that perform semantic code search, tool-augmented reasoning such as grep and static analysis, and multi-file refactoring plans at scale.
Mantis AI is creating a video‑native intelligence layer for media and entertainment companies across Latin America. Through NVIDIA’s Inception program and its collaboration with Google Cloud, Mantis AI operates high‑throughput inference pipelines, rigorously benchmarks fine‑tuned model variants, and experiments with NVIDIA NIM, Blueprints, and vector search services to deliver production‑ready stacks that help broadcasters and streamers move from analyzing video to orchestrating it at scale.
Photoroom specializes in diffusion‑ and transformer‑based vision models that generate and edit product imagery. Photoroom processes over 1,000 images per minute. They use TensorRT for a 4x speedup and xformers to make their Stable Diffusion pipelines 100% faster. They’ve consumed over 500,000 GPU hours on Google Cloud without issue.

Sell agents directly in Gemini Enterprise

For AI startups, navigating enterprise procurement is often a bottleneck to growth. By integrating the Google Cloud Marketplace into Gemini Enterprise, we're providing a global commercialization engine to accelerate procurement.

Today at Next, we’re announcing an exciting group of more than 70 startups who are bringing their agents to our Marketplace.

And, to help even more startups build agents and bring them to market, we are launching a new $750 million dollar fund to support agent development and marketing for our partners. You can read more about this new funding here.

Presenting the Gemini Startup Forum

In June, we’re hosting our Global Gemini Startup Forum, a two-day, in-depth gathering of founders from seed to Series A startups. At the event, attendees will engage with Google leaders and engineers, build and scale products, and share feedback with teams across our AI portfolio. Take a deeper look at the program and register your interest for upcoming editions.

Building on that momentum, we’re also excited to share that applications are open for our next program: the Gemini Startup Forum: Cybersecurity, taking place in London later this year. If your startup is utilizing AI to build the next generation of security solutions, we invite you to apply to join us!

Join the Google for Startups AI Agents Challenge

Startups, it's your chance to get hands-on right away. We’re excited to launch the Google for Startups AI Agents Challenge at Next 2026.

Open to anyone (not just Next attendees), this global competition runs for six weeks and gives teams $500 in credits and the tools — like the new Gemini Enterprise Agent Platform — to build autonomous systems and compete for a share of a $90,000 prize pool.

The challenge, which opens for submissions today, and runs until June 5, 2026, offers three tracks tailored to where you are in your development journey. You can build a net-new agent from scratch, optimize an existing prototype for production reliability, or refactor a business-ready agent for potential enterprise distribution on Google Cloud Marketplace and the Gemini Enterprise app.

Submissions will be evaluated based on the following weighted criteria: Technical Implementation (30%), Business Case (30%), Innovation and Creativity (20%), and Demo and Presentation (20%). Learn more here and sign-up now.

Read the new Future of AI: Perspectives on generative media for Startups report

Our Future of AI: Perspectives on generative media for Startups report compiles exclusive interviews with eight startup founders, investors, and Googlers to capture their candid advice, strategic priorities, and predictions for the industry's future.

Understanding these firsthand founder perspectives will be instrumental in helping you navigate the next wave of AI-driven content creation and build the future of generative media on Google Cloud — and maybe find yourself featured on a list like this at a future Next.

Small and midsize businesses jumpstart their AI transformations with Gemini Enterprise

Wed, 22 Apr 2026 12:00:00 +0000

Small businesses are the backbone of the global economy. With 400 million SMBs worldwide and 36 million in the U.S. alone, they provide 50% of global employment. Now, with Google Cloud AI, they’re scaling faster, operating more efficiently, and delivering better results for their customers.

For years SMBs have been working with cutting edge products like Google Workspace, Google Ads, Search, YouTube, Maps, Google Wallet, and more to grow their business. Now, more SMBs are taking the first big steps in their AI journeys with our leading models and our agentic platform, Gemini Enterprise.

By making AI accessible to every employee, Gemini Enterprise serves as a launchpad for SMBs ready to develop an AI strategy and build new solutions to work smarter than ever before.Businesses are using AI to automate manual tasks and build custom solutions that extend their reach and help them better support their own customers.

We are proud to support these early milestones, and at Next we’re excited to showcase dozens of new customer partnerships with organizations taking their first steps toward an AI-driven future. Read on to explore their success stories and find our guide to the best SMB resources at Google Cloud Next ’26.

How Gemini Enterprise helps SMBs solve complex challenges

Across the globe, small and medium businesses are using AI to help grow their business. Here’s just a few of the many exciting examples of how SMBs are already benefiting from the secure, scalable, enterprise-grade tools in Gemini Enterprise:

Apex Leaders, a US-based business services company, is using Gemini Enterprise to power an internal search engine, providing easy access to internal data sources and automated information summarization and content drafting for consultant teams.
ARCHINERGY, a US based architecture firm, utilized Gemini Enterprise to create an AI agent that answers questions about architectural design and internal documentation, generating faster insights in just a fraction of the time compared to traditional search methods.
Constructora Las Galias, a Colombia-based construction firm, is using Gemini Enterprise to power a centralized internal knowledge platform, providing streamlined sales and legal workflows and immediate employee access to critical business information.
Eficode, which operates as a software development and DevOps firm within Finland, is using Gemini Enterprise to create a multi-platform support engine that quickly indexes complex internal wikis, so employees can provide customers with faster technical support
Grant Thornton Chile, a leading audit and consulting firm, is using Gemini Enterprise to enhance its agile management by powering secure financial data validation, accelerating HR and sales questions, and enabling unified search across disparate document types.
Grupo Ruiz, a leading Spanish company in sustainable mobility, uses Gemini Enterprise to improve team productivity and process efficiency, achieving an optimized workflow and greater data visibility
Huge, an American business services agency, utilized Gemini Enterprise to create AI agents that automate market research and contract analysis, generating new business intake in just minutes compared to several days.
Incrementa, a Chilean marketing company, is using Gemini Enterprise to automate client reporting and data analysis, helping it scale personalized service and boost campaign reach by 40%.
Koufu, one of Singapore's largest food and beverage companies, which operates and manages a large network of food courts and coffee shops, is using Gemini Enterprise to automate sales reporting and BigQuery data analysis. This reduces spreadsheet bottlenecks so shop managers can focus on front-of-house service quality.
La Maison du Whisky (whisky.fr), a specialized spirits and beverages retailer in France and internationally, chose Gemini Enterprise to create a 'Digital Sommelier' that transforms technical product data into high-end storytelling and marketing copy in seconds
SAI360, a US-based software company, is using Gemini Enterprise to power the design, production, and localization of ethics training, achieving 99% faster course creation and a 95% reduction in training media production costs.
Tauá Resorts group is using Gemini Enterprise to create an intelligent agent that generates real-time insights and dialogue suggestions. This technology allows the sales team to answer complex questions instantly, leveraging data from previous events to craft the perfect proposal for every client.
Tirol, a leading dairy producer in Brazil, is using Gemini Enterprise to create an interactive knowledge base, providing democratized data access and enhanced visibility across its entire value chain.
Wimberly Allison Tong & Goo leverages Gemini Enterprise to streamline digital planning, landscape, architecture and interior design workflows and visual queries, enabling the International Hospitality Design firm to accelerate the transitions from idea generation to design concepts and high-fidelity visualizations.

Practical Resources for Lean Teams at Next ‘26

At Google Cloud Next 2026, we are showcasing how SMBs can enhance their impact by integrating AI into their daily workflows. For attendees focused on maximizing impact with focused resources, we have curated a dedicated track to help you navigate the AI era.

When browsing the Session Library, use the Small IT Team filter to find deep dives into:

Cost Optimization: How to deploy AI effectively and manage resources on a budget that’s right for your goals.
Low-Code AI: Training on how to accelerate your business goals by building custom apps and agents.
Adaptive Security: Proactive defense strategies designed to provide enterprise-grade protection regardless of your security team's size.

Get Started Today

If you can’t make every session, we’ve made it easy to keep the momentum going:

The AI Starter Kit: A streamlined, four-step guide to deploying Cloud AI across your organization.
Monthly AI Newsletter: Sign up to receive the latest industry highlights and Cloud AI updates all year long.

Check out the full SMB guide here and start planning your itinerary. See you there!

Oracle AI Database@Google Cloud: The foundation for the Agentic Enterprise

Wed, 22 Apr 2026 12:00:00 +0000

For decades, many of the world’s most critical enterprise datasets have relied on the performance of Oracle databases. Today, we are bringing that reliability even closer to the cutting edge. By enabling customers to run Oracle AI Database services natively within Google Cloud, we’ve bridged the gap between foundational data and the modern AI stack.

With the latest wave of upcoming launches for Oracle AI Database@Google Cloud, we aren't just making it easier to migrate; we are building a direct pipeline from your Oracle systems of record to the insight layer of Google Cloud. By bringing mission-critical data easily and securely to Gemini models and BigQuery, customers can transform static records into autonomous, agentic workflows.

New capabilities announced at Next ‘26

Here is a breakdown of the key new features designed to strengthen your Oracle-to-agentic- AI strategy:

New regions launched: We have significantly expanded the availability of Oracle AI Database@Google Cloud, across 15 regions (and 20 sites) globally. The recent rollout included key global hubs such as Milan, Iowa, São Paulo, Tokyo, Sydney, and Mumbai, among others. With additional regions like Mexico and Turin coming soon, this expansion ensures higher availability and lower latency for your mission-critical workloads across the globe for our Google Cloud customers.
Enhanced AI capabilities: This is the foundation for agentic AI. We are introducing the preview of Managed MCP Server for Oracle workloads, which allows agents like Gemini to interact directly and seamlessly with your Oracle infrastructure. Building on this, the new Oracle AI Database Agent, available in the Google Cloud AI Agent Marketplace, lets you talk to your Oracle data directly from Gemini Enterprise — no custom chatbot or NL-to-SQL solution required.
Database Center integration (Generally Available): To move at the speed of AI, your infrastructure must be healthy and visible. Database Center now supports Oracle AI Database@Google Cloud, providing a "single pane of glass" for your entire data estate. Whether you are running Exadata or Autonomous Database, you can now monitor your inventory and streamline operations through a unified experience within the Google Cloud console.
Knowledge Catalog integration (Preview): Data discovery is the first step toward intelligence. By extending the Knowledge Catalog to Oracle AI Database@Google Cloud, we are breaking down the walls between your Oracle systems and the Google Data Cloud. This allows for a unified governance and metadata layer, making it easier for customers to find, trust, and use Oracle data and provide context to AI agents.
OCI GoldenGate Service integration (Preview): Real-time data is the lifeblood of AI. This integration enables low-impact, continuous data movement, allowing you to streamline migrations from on-premises environments to Oracle AI Database@Google Cloud. In addition, it provides a live link to BigQuery, enabling operational data analytics that reflect the "here and now" of your business.
VPC Service Controls: Oracle AI Database@Google Cloud administrators can use VPC Service Controls to restrict access to the admin API and create databases within a service perimeter. VPC Service Controls protect businesses from unauthorized access outside the security perimeter, even if credentials have been compromised.

The agentic future

The goal of these integrations is simple: To make your data active. When your Oracle data resides natively in Google Cloud, Gemini doesn't just “talk about” your data — it can work with it. Whether it's an AI agent forecasting supply chain shifts in BigQuery based on live Oracle ERP feeds, or a customer service bot with real-time access to legacy account history, the data vault is more open, accessible, and valuable than ever (while remaining just as secure).

Hear directly from our customer, Banco Actinver, regarding the transformative impact of relocating their Oracle data to Google Cloud.

Get started

You can access Oracle AI Database@Google Cloud through the Google Cloud Marketplace using your existing Google Cloud account and billing.

For more information, visit:

Building the Agentic Enterprise with Google Cloud partners and a $750M innovation fund

Wed, 22 Apr 2026 12:00:00 +0000

We are now seeing the Agentic Enterprise become reality for customers, and this week at Next ‘26 we are announcing exciting, new innovations to help customers accelerate agentic AI even further.

Our partners play a critical role in enabling the Agentic Enterprise, and today we are also announcing new resources, technologies, and deep technical partnerships to ensure we offer customers the industry’s most capable partner ecosystem for the agentic era, including:

A $750 million partner fund for agentic development applicable across global consulting firms, software partners, and our channel partners.
New ways for customers to deploy partner agents in Gemini Enterprise.
Deeper and more technical partnerships with global consulting firms to support customers, including with new teams of Google forward deployed engineers.
Integrating Gemini models more deeply into enterprise platforms from Palantir, Salesforce, SAP, ServiceNow, and more.
More AI-powered features in Google Cloud Partner Network to help our partners deliver high quality services.

Investing to accelerate AI agent development

We are committed to offering customers the most AI-capable partner ecosystem in the industry. To empower our partners to drive real transformation in the agentic AI era, we are launching a $750 million innovation fund to accelerate agent development and deployment globally, applicable to every business process, function, and industry.

This funding will support a wide range of activities including:

Hands-on support for software companies to build AI agents into their products with the Gemini Enterprise Agent Platform and bring them to market through our Agent Marketplace and through the new Agent Gallery in Gemini Enterprise.
Expert Google forward deployed engineers (FDEs) who will partner with major systems integrators to help their customers solve deep technical challenges and deploy Google AI more rapidly.
Deployment and usage incentives to help services partners thrive in the agentic era.
Training, technical development initiatives, and workshops to help partners build and deploy agents for customers using Gemini Enterprise Agent Platform.

Surfacing partner-built agents in Gemini Enterprise

At Next, we’re announcing Gemini Enterprise Agent Platform, a comprehensive platform to build, scale, govern, and optimize agents. It includes Agent Gallery, where customers can browse a highly vetted set of agents built by our partners.

Today, Agent Gallery provides access to a wide range of third-party agents. These agents have been built on top of our secure, enterprise-grade infrastructure, meaning customers can deploy them within their businesses with the highest levels of governance and confidence. Today, this includes agents built by Accenture, Adobe, Atlassian, Deloitte, Lovable, Oracle, Palo Alto Networks, Replit, S&P Global, Salesforce, ServiceNow, Workday, and more.

Empowering global consulting partners to drive AI transformations

Today, Google Cloud’s global consulting and systems integrator partners offer customers more than 330,000 experts trained on implementing Google AI. At Next, we are expanding our partnerships with every major systems integrator, including:

Accenture is helping enterprises drive AI-powered reinvention and business value faster and at scale with the launch of a first-of-its-kind Gemini Enterprise Acceleration Program. The program brings elite engineering and forward deployed engineers from Google Cloud and Accenture directly to customers.
BCG is expanding its partnership with Google Cloud to accelerate Gemini Enterprise transformation, helping organizations deliver at-scale agentic adoption.
Capgemini is establishing a Google Cloud AI Enterprise Hub to accelerate enterprise-scale adoption of Gemini Enterprise.
Cognizant is launching a dedicated Gemini Enterprise practice group to accelerate enterprise adoption of Gemini Enterprise.
Deloitte is forming a dedicated Google Cloud Agentic Transformation practice focused on Gemini Enterprise and will roll out Gemini Enterprise to more than 100,000 of its own teams.
HCLTech is launching a Gemini Enterprise Business Unit to accelerate the development and adoption of industry-specific solutions built on Gemini Enterprise.
Infosys is leveraging Gemini Enterprise within its Infosys Topaz platform and is equipping more than 100,000 Infosys developers across Infosys’ global delivery teams with Gemini Enterprise.
KPMG is deploying Gemini Enterprise with a life sciences company and launching a new Financial Close Companion agent built with Workday and Google Cloud.
Kyndryl is deepening its Google Cloud partnership with expanded Google Distributed Cloud services for sovereign, AI-ready applications.
McKinsey is launching the McKinsey Google Transformation Group to accelerate enterprise AI outcomes with Gemini Enterprise, combining its strategic expertise with Google's AI stack to help organizations scale agentic transformation
PwC is launching a dedicated Google Cloud AI Center of Excellence to help organizations scale AI adoption, pairing industry expertise with Gemini Enterprise to deploy AI agents that reason, act, and automate processes at scale.
TCS is launching new agentic AI offerings and a dedicated Gemini Enterprise practice, featuring more than 3,000 industry-focused AI agents and an expanded global network of Gemini Experience Centres to accelerate AI-native, autonomous enterprise operations.

Additionally, we will begin to embed teams of Google Cloud engineers with a subset of global partners, including Accenture, Capgemini, Cognizant, Deloitte, , HCLTech, PwC, and TCS in order to help their customers more rapidly prototype and deploy AI agents within their businesses. AI-native services partners, including Altimetrik, Artefact, Covasant, deepsense.ai, Distyl.ai, Northslope, Quantium, Tribe.ai, and Tryolabs will launch Gemini Enterprise practices, receiving credits for sandbox development, technical upskilling, and referral opportunities.

We are also rolling out a new program offering early model access for a select group of partners, including Accenture, Bain & Company, BCG, Deloitte, and McKinsey, who will be able to preview and begin building with pre-release versions of upcoming Google DeepMind models.

Bringing Gemini to More Customers through Popular SaaS Platforms

Already, many of the world’s leading agentic SaaS and AI platform companies integrate Gemini into their products. At Next, we’re expanding these integrations even further, including:

Atlassian is bringing Gemini 3 Flash to Rovo and integrating multimodal capabilities into Remix in Confluence, helping teams instantly transform text-based documentation into high-fidelity diagrams and charts for faster stakeholder decision-making.
Box is launching new Box Agents powered by Gemini 3 Flash and Gemini Enterprise, helping enterprises transform static files into actionable intelligence by natively integrating AI orchestration into their secure content management workflows.
DocuSign is using Gemini to power new features that summarize complex agreements, identify key clauses, and help users understand the implications of their contracts.
Oracle is launching the Oracle AI Database Agent for Gemini Enterprise. This new agent enables end users to ask business questions of their Oracle data in natural language in Gemini Enterprise, without needing to write SQL or understand the underlying data model.
Palantir is adding Gemini and BigQuery integrations for commercial customers, enabling customers to connect best-in-class models to their most critical AI workflows and operations.
Salesforce is adding native Gemini support to its Atlas Reasoning Engine. This enables Agentforce to “see” across text, image, and video formats, drawing from years of customer history to accurately solve complex problems. This means faster, smarter resolutions, building upon the success thousands of customers are already seeing from Gemini within Agentforce to build prompts.
SAP is integrating Gemini Enterprise into its Engagement Cloud to deliver AI-powered customer service and sales insights alongside creative tools for image and text generation.
ServiceNow is integrating its AI agents with Gemini Enterprise, bringing autonomous operations to the world’s largest enterprises

Building a partner channel for the agentic era

Our new partner program, Google Cloud Partner Network, is designed to help partners thrive in the agentic era. Last year, we used AI to unlock deep insights across our partner tools; now, we are building the agentic workflows that turn those insights into autonomous growth. Key updates include:

The Partner Agent: Integrated into the Partner Network Hub, this agentic tool acts as a central orchestrator for the partner experience. Beyond answering questions, it actively guides partners on next steps, summarizes complex assets, and provides real-time coaching for registrations and statements of work.
The Agentic Earnings Hub: Here, partners can find new capabilities to auto-draft statements of work and monitor consumption milestones to auto-generate claim requests. When paired with the Earnings Potential Modeler, these tools provide contextual recommendations to map every available incentive down to the individual client level.
Partner Finder: We are also extending this intelligence to customers, turning discovery into a conversational experience where natural language prompts pinpoint the ideal partners for the most hyper-specific workloads.

Finally, we’re honored to highlight the winners of Google Cloud’s 2026 Partner Awards, which celebrate the transformative impact and incredible value our partners have delivered for customers over the past year. Our ecosystem continues to evolve to meet the needs of businesses across every industry, and we are constantly impressed by their ability to solve complex, global challenges using our technology. To discover more about these exceptional achievements, please read our full list of partner award winners.

I can’t wait to meet with thousands of you this week to build the future of the Agentic Enterprise!

AI & Machine Learning

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Cloud CISO Perspectives: At Next ‘26, why we’re multicloud and multi-AI

Cybersecurity in the era of the agentic enterprise

In case you missed it

Threat Intelligence news

Now hear this: Podcasts from Google Cloud

The founder’s AI foundation: The top announcements for startups from Next ‘26

1. Gemini Enterprise: comprehensive agent platform

2. The Agentic Data Cloud: Data that actually does the work

3. Next-Gen Compute: Performance and Cost-Efficiency

4. Agentic Defense: Security as Your Enterprise Go-To-Market Engine

5. The Go-To-Market Engine & Ecosystem Capital

Start building with the AI Agents Challenge

UKG unlocks real-time workforce intelligence at scale with the Agentic Data Cloud

Unifying the systems behind the suite

Bringing people intelligence to intelligent people

Driving impact across every layer

Learn more

50+ fully managed MCP servers now available for Google Cloud services

MCP built for the enterprise

Case study: Insta360 redefines video editing

Broad coverage across the Google ecosystem

1. Infrastructure, operations, and security

2. Databases, analytics, and storage

3. Services and apps

Technical scenario with demo

Start building today

Day 2 at Google Cloud Next: A marathon developer keynote

Gemini Enterprise Agent Platform: A warm up

First off the blocks: Building the agent

An agent to evaluate the agent

Agents that never forget

When agents go off course

Scaling the agents

Shifting down

The finish line

Day 1 at Google Cloud Next ‘26 recap

Gemini Enterprise: The end-to-end system for the agentic era

1. Gemini Enterprise Agent Platform

2. Gemini Enterprise app

3. AI Hypercomputer

4. Agentic Data Cloud

5. Agentic Defense

6. Workspace Intelligence

7. Agentic Commerce

Innovate all the things

Enabling the agentic enterprise: business and industry agents arrive in Gemini Enterprise

Meet the agents your organization can start using today

What’s new in GKE at Next ‘26

GKE Agent Sandbox: Accelerating the agentic era

GKE hypercluster redefines the scalability ceiling

Supercharging state-of-the-art inference

Eliminating RL compute bottlenecks

Intent-based autoscaling on custom metrics

New workloads, same mission

Introducing Virgo Network, Google’s scale-out AI data center fabric

Reimagining the data center network

Introducing Virgo Network: Megascale data center fabric

Improving reliability at scale

The foundation of the AI Hypercomputer

Introducing Gemini Enterprise Agent Platform, powering the next wave of agents

How customers are achieving more with Gemini Enterprise Agent Platform

Build AI agents

Scale AI agents

Govern AI agents

Optimize AI agents

Next ‘26: Redefining security for the AI era with Google Cloud and Wiz