Partners

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.

Announcing new partner-supported workflows for Google Security Operations

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

Security teams are frequently burdened with manually stitching together telemetry, alerts, and response playbooks. This fragmentation can limit visibility, increase alert fatigue, and slow down investigations.

Defending the modern enterprise requires tools that work together. Today at Google Cloud Next, we are thrilled to announce a robust cohort of new partner integrations for Google Security Operations as part of the Google Cloud Security integration ecosystem.

Designed to deliver high-fidelity security workflows right out of the box, our newest partners to join our ecosystem with more than 300 vendors include: Beacon Security, Contrast Security, Darktrace, Gigamon, GreyNoise, Intezer, Prophet Security, SAP, Synqly, Thinkst, Tidal Cyber, Torq, and Vali Cyber.

Here’s how our partners are building in the Google Security Operations ecosystem, the integration types supported, and how security operations centers (SOC) can use them.

Specificity and depth: Supported integration types

The Google Security Operations platform supports several distinct integration patterns. Here is how our current cohort is using these architectures to deliver specific technical capabilities:

1. Data feed integrations for deep visibility across your stack
These integrations pipe crucial telemetry directly into the Google Security Operations data lake, pre-mapped to our unified data model (UDM) schema so your team doesn't have to write custom parsers:

Beacon Security: Architects ingestion for both normalized and raw data. Beacon expands your coverage by collecting data from sources including APIs, syslog, webhooks, and cloud storage. Using a real-time streaming pipeline, it normalizes these raw events directly into out-of-the-box UDM mappings in minutes. Before data even reaches Google Security Operations, Beacon applies security-driven data reduction to filter and aggregate events preserving detection fidelity. Finally, it uses AI-powered data orchestration and continuous security data posture management to track collection health and help reduce the risk of blind spots becoming breaches.
Contrast Security ADR: Detects, investigates, and responds to application-layer attacks with the Contrast ADR and Google Security Operations integration. Verified runtime attack telemetry streams into Google's UDM, powering purpose-built detection rules that automatically surface confirmed exploits as cases and correlate application-layer findings with signals from WAFs, EDR tools and database security sensors.
Gigamon GigaVUE Cloud Suite: Introduces a new integration to help organizations close visibility gaps across hybrid cloud environments. This integration amplifies the power of Google Security Operations with actionable application and network-derived telemetry — including packets, flows, and metadata — from Gigamon, giving teams the context they need to detect threats earlier and investigate with greater precision.
SAP Logserv: Closes the visibility gap between SAP Logserv and security operations, empowering analysts to detect, investigate, and respond to SAP-specific threats alongside their existing IT landscape. The integration features out-of-the-box ingestion and uses SAP-specific standard parsers to normalize raw, complex infrastructure and application logs into the UDM format. This gives teams unified, enterprise-wide visibility to defend business-critical data while reducing the need for deep SAP technical expertise or custom log pipelines. This integration has been developed by Google, in partnership with SAP.
Synqly Mesh: Offers a unified API that performs bi-directional data normalization between Google Security Operations' UDM and the Open Cybersecurity Schema Framework (OCSF). It supports event ingestion configurations (Sink) as well as full bi-directional SIEM connectivity.
Vali Cyber Zero Lock: Streams hypervisor-level security events directly into your existing Google Security Operations workflows. This integration provides visibility into emerging ESXi threats and is designed to help keep virtual infrastructure protected and operational.

2. Response integrations for streamlined alert and case management
These integrations hook directly into your workflows, allowing external platforms to trigger alert delivery, create cases, and execute automated actions.

Darktrace: Currently in development, this response integration enables Google Security Operations to ingest Darktrace Incidents and Model Alerts. By pulling in pre-parsed raw logs via API or webhook, this integration provides your team with network context needed to streamline alert delivery, manage cases, and trigger automated response actions.
GreyNoise: New integrations that enhance detection and response capabilities in Google Security Operations. Spanning both SIEM and SOAR, the integration delivers standardized indicator ingestion, pre-built dashboards, YARA-L detection rules, saved searches, webhook support, response actions, and ready-to-deploy playbooks.
Thinkst Canary: Integrates directly with Google Security Operations SOAR, allowing security teams to ingest high-confidence Canary incidents as actionable cases. It preserves full alert context, surfaces extracted entities like IP addresses and hostnames, and allows analysts to acknowledge incidents without ever leaving their Google Security Operations workflow.
Torq: Brings its AI SOC Platform to Google Security Operations to help automate the threat lifecycle. Torq pulls detections directly via API, applies agentic AI auto-triage to filter out noise, and executes autonomous response actions — like isolating endpoints or revoking access — across the security stack while keeping Google Security Operations updated with case status.

3. Pulling Google Security Operations data (bi-directional API workflows)
Security doesn't just happen in one console. These integrations use secure APIs to pull Google Security Operations detections and intelligence natively into partner platforms, bridging the gap between tools.

Intezer: Allows you to natively query, investigate, and triage Google Security Operations detections without leaving your established environment. It automatically ingests Google Security Operations alerts directly into Intezer, which then queries your underlying Google Security Operations data during active investigations to drive autonomous triage. This bi-directional workflow ensures your team has the full picture — eliminating the need to pivot between consoles, reducing manual data gathering, and freeing your analysts to focus on high-level decision-making and rapid response.
Prophet Security: Integrates with Google Security Operations to provide AI-powered alert investigation and natural language threat hunting. It is designed to automatically ingest alerts, queries the Chronicle API for real-time UDM event context, and bidirectionally syncs investigation findings and comments back to Google Security Operations, with the goal of reducing analyst workload.
Tidal Cyber: Pulls configuration and policy data from your cyber defense intelligence (CDI) environment. It can retrieve ATT&CK-mapped curated detection rules and user-created rules from Google Security Operations. It also synchronizes the detection rules states with Tidal to reflect enabled and disabled capabilities. By knowing both what a product is capable of and what's currently enabled in your environment, Tidal helps identify configuration gaps and assists in keeping your defensive stack and coverage map accurate as policies change.

Details on all partner integrations can be found in our technical documentation or in your Google Security Operations Content Hub console.

Unify your defense today

For technology vendors and developers looking to join the Google Cloud Security integration ecosystem, you can get started by downloading the Google Security Operations Build Partner Guide to understand our UDM schema and API requirements, and reach out to our Google Cloud Security Tech Partners team to request a development environment to accelerate your build in time for our next release cycle.

You can follow all of our security announcements at Next ‘26 here.

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:

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, 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!

Announcing our 2026 Google Cloud Partners of the Year

Tue, 21 Apr 2026 19:00:00 +0000

Google Cloud is honored to present the 2026 Partner of the Year winners!

These awards recognize partners who have displayed a commitment to excellence by leveraging Google Cloud technology to deliver innovative solutions that drive customer success. This group represents a breadth of industries and countries, showcasing the power of collaboration to produce impactful results and revolutionize work worldwide.

Please join the entire Cloud community in congratulating our winners for the following categories:

Global

These awards honor global partners that exemplify excellence in their category, driving innovation and delivering industry-leading solutions with Google Cloud.

Artificial Intelligence

Recipients in this category have helped customers leverage AI to achieve outstanding success through Google Cloud.

Data

Winning partners in this segment have expertly deployed new Google Cloud data analytics or database solutions to help customers extract actionable insights from their data, create valuable inputs for AI, and fuel business transformation.

Infrastructure Modernization

Partners receiving these awards have helped their customers modernize their infrastructure by leveraging Google Cloud, resulting in increased agility, scalability, and cost-efficiency.

Security

This category honors partners that have effectively implemented Google Cloud's security solutions, safeguarding their customers' data and infrastructure from evolving threats.

Google Workspace

Recipients in this segment excel in driving sales and delivering outstanding services for Google Workspace, empowering customers with innovative solutions that transform collaboration and productivity.

Services & Industry Solutions

These award winners have leveraged Google Cloud to create comprehensive and compelling solutions that made a significant impact in an industry across multiple regions.

Technology & Business Applications

Honorees in this category are being recognized for building innovative global technology and application solutions on Google Cloud, solving complex business challenges, and driving significant impact across industries.

Country

This segment celebrates partners that have demonstrated exceptional expertise in leveraging their services and solution capabilities to drive sales and deliver outstanding outcomes for Google Cloud customers in marquee markets.

Marketplace

These winning partners have excelled in delivering innovative solutions through Google Cloud Marketplace, significantly accelerating customer adoption and driving business growth for joint customers.

Public Sector

Recipients in this category have provided exceptional service and enabled the success of their public sector customers by innovating, building, and delivering the right combination of Google Cloud solutions.

Talent Development

Winners in this segment have shown a commitment to growing and promoting their team's cloud skills through training, upskilling, and reskilling their workforce on leading-edge cloud technology with Google Cloud certifications.

Training

Partners receiving this honor have provided exceptional training services and enabled customer success by innovating, building, and delivering the right combination of Google Cloud solutions through learning.

Other categories: Distribution, Geospatial, and Social Impact

These awards recognize partners who have delivered exceptional results in specialized domains—from leveraging geospatial technologies for location-based insights, to driving market expansion through distribution, to creating meaningful social impact via innovative community-focused initiatives.

Congratulations once again to our 2026 Google Cloud Partner of the Year winners. Thank you for your invaluable contributions and continued partnership. Here’s to another award-worthy year of collaboration in the cloud.

GEAR up to get the most out of AI learning at Google Cloud Next ‘26

Tue, 21 Apr 2026 15:00:00 +0000

Our new GEAR program, powered by Google Skills, equips every professional with the hands-on AI training needed to build and launch enterprise-ready agents at scale. Anyone can join GEAR for access to AI training and more, including:

35 monthly learning credits to use towards courses and hands-on labs in Google Skills
Discord community access to connect with peers
Curated agentic news and resources from Google
Google Cloud customers can join the Get Certified program to prepare for Google Cloud certifications at no cost

If you really want to get hands on with GEAR, we’re excited to share that it will also have a major presence at Google Cloud Next ‘26. We’ve integrated GEAR throughout the event, making it easy for you and your teams to build skills on site and jumpstart your GEAR journey through new mini-labs, learning paths, and hands-on workshops from the experts at Google.

Next learning experiences you can’t miss

Next will feature the latest new GEAR learning paths, available on Google Skills, including: Introduction to Agents and Google’s Agent Ecosystem; Develop Agents with Agent Development Kit (ADK); Deploy Production-Ready Agents; and Scale Agents Across the Enterprise.

We also have a collection of exciting areas where you can engage with GEAR programming.

The Skills Challenge: Your on-the-ground gamified experience. With more than 20 activities designed to help you get the most out of Next, you can join thousands of players, rise up the leaderboard, and earn exclusive swag.
Google Skills Zone: Choose your own learning adventure. Drop in for 10- to 15-minute mini-labs, complete an AI Boost Bites lesson to gain new AI skills, or deep dive into technical workshops inspired by our premium learning services and Google Skills content.
Real-world, immersive sessions: Step into specialized two-hour labs where you’ll build analytics agents to analyze sports data from Major League Baseball or McLaren. Everything you learn can be applied to your own business challenges, too.
Breakouts: Join our expert-led sessions and panel discussions featuring real-world customer stories. Don’t miss these stand-out sessions (you can even add them to your Next agenda):
- AI upskilling strategies from the world's most innovative retailers featuring Wayfair and Woolworths
- Scale your workforce with AI: Real-world lessons with Salesforce, Palo Alto Networks, and EVERSANA
- Building an agent-ready workforce
- Bridging the gap: Scaling AI readiness with Google certifications

“GEAR does a great job introducing developers to the fundamentals of building AI agents and agentic systems. As someone working with enterprise platforms, I especially appreciate how it helps developers move beyond simple prompts to think in terms of tools, workflows, and scalable agent architectures.” – Jitendra Gupta, Google Developer Expert

Jumpstart your learning with GEAR and Google Skills today

Don't wait until Google Cloud Next. Stop prototyping and start shipping, today. Sign up for GEAR to get no-cost access to agentic training and more on Google Skills.

Building the agentic future: A spotlight on Google Cloud’s media & entertainment partner ecosystem

Thu, 16 Apr 2026 13:15:00 +0000

As we gather in Las Vegas for NAB Show 2026, the industry conversation has shifted. We are no longer asking if AI works; we’re now focused on how it scales. The era of AI experimentation is over — production-grade execution is here.

At Google Cloud, we believe no studio or broadcaster should have to build this future in isolation. Our mission is to provide the agentic platform and AI and cloud tools that allow our partners to innovate at the speed of ideas — from the tools used in the edit suite, to the technology that delivers video to millions of viewers worldwide.

Enhancing production: From manual tasks to intelligent assistants

Modern creative workflows are often slowed down by manual technical tasks. Google Cloud is working with ecosystem leaders to integrate advanced AI capabilities directly into the core of production software, so creators can focus on their artistry, not tedious tasks. With AI acting as a proactive assistant within the creative suite, production teams can significantly reduce the time between a raw idea and a finished frame.

Avid: With the launch of Content Core on Google Cloud, Avid is delivering a truly cloud-native studio. And by integrating multimodal AI search into Media Composer, editors can find the exact frame they need using natural language, turning hours of logging into seconds of discovery.
Backlight: Backlight makes complex media workflows simple for teams of all sizes, from production through monetization. Built on Google Cloud with the Video Intelligence API, Backlight's Iconik platform automatically adds searchable metadata upon upload. Customers see up to 50% shorter production cycles and save up to 60% on storage by deeply understanding their media libraries, reducing duplications, and optimizing asset placement.
Brahma.ai: Brahma AI, an enterprise AI content platform, is powering high-fidelity digital likenesses across retail, entertainment, and healthcare, making them interactive and intelligence-driven within a secure and governed framework.

Unlocking content value: From static archives to active assets

Data is only as valuable as the insights you can extract from it. Our partners, listed on the Google Cloud Marketplace, are using generative media models to transform massive, static archives into searchable, revenue-generating engines. By making every frame discoverable, we’re helping media companies turn decades of history into immediate opportunities.

Ateme: Ateme helps simplify global distribution with its new generative AI-powered subtitling solution, which can significantly reduce the manual labor of localizing different media types.
Perfect Memory: Perfect Memory helps customers turn traditional storage into a context-aware knowledge engine. The platform understands the relationships between athletes, historical events, and emotional nuances — transforming massive media archives into an intelligent library that lets creative teams instantly surface the perfect content for any story.
VionLabs: Working with companies like Cineverse, Plex, and Crunchyroll, Vionlabs uses AI to analyze and index content libraries — making video assets more accessible and enabling metadata generation. By understanding the specific mood and aesthetic of each scene, Vionlabs helps streaming platforms move beyond basic genre tags toward more nuanced, sentiment-driven content discovery and marketing.

Scaling global reach: From simple streams to audience growth

To succeed today, media companies must provide a smooth viewing experience and easy payment options. Our ecosystem provides the tools to grow a company’s reach and maximize the value of every subscriber through reliable, high-quality delivery.

Bending Spoons: By leveraging the global scale of Google Cloud, Bending Spoons’ properties such as Brightcove and Vimeo are delivering professional-grade tools for large enterprises, SMBs, the next generation of creators, and more. Its platforms ensure that high-quality video production and distribution are accessible to everyone, from global brands to independent storytellers.
Bitmovin: Bitmovin enables streaming services to scale efficiently while delivering a premium experience across the widest range of devices. By combining real-time observability with AI-driven insights, media teams can proactively optimize engagement and monetization. Furthermore, Bitmovin’s advanced encoding ensures superior visual quality at lower bitrates, supporting everything from high-demand Video on Demand (VOD) to massive, 24/7 live events.
Evergent: Evergent automates complex billing and monetization workflows for AI-powered revenue management. Media and telecommunications companies can use Evergent’s tools to maximize subscription growth and improve long-term customer retention through personalized and agile payment offers.
Harmonic: Harmonic is helping major broadcasters like Grupo Globo modernize their operations. By integrating new digital broadcast capabilities into their cloud-based streaming solutions, Harmonic provides leaders with a faster, more efficient path to manage video processing and delivery at a massive scale.

Ensuring reliability: From infrastructure to a foundation of trust

High-quality content requires a high-performance foundation. We are partnering with infrastructure leaders to ensure that even the most complex global broadcasts remain stable, secure, and responsive.

Zixi provides the broadcast-grade transport and workflow automation needed to move professional video across any network. By offering centralized control and complete visibility into the delivery process, Zixi ensures that leaders like Fubo can manage high-stakes, broadcast-quality live events without the risk of a signal drop.

Visit the ecosystem in action

The strength of our ecosystem is its integration across all aspects of the media and entertainment landscape. From the cameras, to the cloud, to the viewers' screens, these partners represent the future of a more creative, efficient, and agentic media industry.

Visit the Google Cloud Booth (West Hall, #W2731) at NAB Show from April 19-22 to see many of these partners in action through live demonstrations and theater sessions.

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Cool stuff Google Cloud customers built, April edition: BMW big on SLMs, MLB’s Scout Insights AI, personalized resort experiences

Wed, 15 Apr 2026 16:00:00 +0000

AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, who are building the future on our platform, there would be no Google Cloud. In this regular round-up, we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories.

For our latest edition, we learn why BMW Group is experimenting with small language models (SLMs); catch AI-powered commentary from Major League Baseball; hit the slopes with Vail Resort’s AI concierge; build an intelligent grid with CTC Global; witness how ID.me created secure global scale; and see how Manhattan Associates supply chain tools now handle 1 billion daily API calls.

Be sure to check back next month to see how more industry leaders and exciting startups are putting Google Cloud technologies to use. And if you haven’t already, please peruse our list of 1,001 real-world gen AI use cases from our customers.

BMW tests the big potential of small models

Who: As one of the world’s leading providers of premium cars and motorcycles, BMW Group is always at the forefront of automotive technology. This ethos pushed the company to test what type of AI language models are ideally suited to driving situations, where access to cloud-based LLMs isn’t always possible.

What they did: BMW Group wanted to explore the potential of small language models (SLMs), which could run within the limited hardware on a vehicle. Finding the right trade-off between size and capability requires careful optimization, and the sheer volume of viable combinations renders manual searches for the optimal configuration an incredibly tedious, if not impossible, undertaking. To overcome this challenge, BMW and Google built automated, reproducible workflows through executable pipelines using Vertex AI.

Why it matters: The path from a general-purpose LLM to a specialized SLM isn’t straightforward. Every choice — from type of quantization to characteristics and contents of the fine-tuning domain-specific dataset — affects the quality and efficiency of the final model. This creates an exponential range of configurations, each with different trade-offs. It’s a great example of using AI to scale an optimization problem for other AI.

Learn from us: “With automated pipelines, we can rapidly adapt models to our domain and rigorously test and evaluate them against domain-specific benchmarks. This allows us to iterate and optimize models in hours rather than days.” – Dr. Céline Laurent-Winter, vice president, Connected Vehicle Platforms at BMW Group

MLB Scout Insights: AI-powered color commentary

Who: Major League Baseball is famous for its colorful announcers. Now, MLB is bringing more baseball color straight to your pocket, and Gemini is helping give it a voice.

What they did: Each season, millions of baseball fans use the MLB app and tap over to the Gameday feature for live, up-to-the-pitch action across more than a dozen games. Starting this season, the league launched MLB Scout Insights in Gameday, which uses Gemini models to quickly scan decades of game and player data, cross-references it with situational game scenarios, and then delivers game-relevant context during key matchups.

Why it matters: Given the sport’s storied history, 162-game regular season, and global reach, baseball fans are among the most sophisticated and passionate out there. To keep them engaged with Gameday and the MLB app, the league wanted to deliver insights that truly felt meaningful and interesting. Building the tool meant answering a rather squishy question: What makes an insight actually insightful, not just an accurate fact, and how can an AI learn that distinction? The answer came from some clever “surprisal” analysis.

Learn from us: "With Scout Insights, every fan can feel like the smartest person in the stands, at the water cooler, or on the couch. It’s about deepening connections to the game, and sharing that passion with others. That’s the magic of sports, and we’re making more of it possible with the magic of AI." – Josh Frost, senior vice president of product & Matt Graser, director of engineering, Major League Baseball

Vail Resorts makes personalized AI assistance easy

Who: Vail Resorts operates some of the most iconic and beloved mountain destinations in the world, including Whistler Blackcomb, Park City Mountain, Stowe, and Crested Butte.

What they did: Vail Resorts launched My Epic Assistant during the 2024-2025 snow season, and expanded it this year to add even more AI-powered chat features powered by Google’s powerful Gemini models. The result is an agentic guide to the slopes that can help skiers and snowboarders decide on the right season pass, share the latest snow report, check on lesson preparations, or suggest a good stop for cocoa.

Why it matters: Vail Resorts wanted more than a chatbot; they wanted a digital concierge that understands the nuance between a powder day at Whistler and a family trip to Beaver Creek. As the company implemented and refined personalization, improved search, summary capabilities, and conversational flow within My Epic Assistant, the app has delivered a 45% reduction in escalation to human agents since launch.

Learn from us: "Utilizing tooling from Google Cloud, we could lean into agentic design patterns that gave us a way to unlock natural, personalized conversations. These boosted customer satisfaction, while reducing the need for manual intent design. These tools also let us combine flexibility and control to enable the assistant to respond fluidly but always within the boundaries of our brand, policies, and product strategy.”— The Vail Resorts technical team

CTC Global turns the smart grid into an intelligent one

Who: CTC Global is a leading manufacturer of advanced transmission conductors and power lines. While many nodes in the grid contain IoT sensors, it recognized a literal gap in the transmission lines themselves.

What they did: CTC’s new GridVista platform threads fiber-optic cable into its high-strength carbon fiber composite core, and connects these to monitoring technology built with AI and monitoring technology from Google Cloud and Tapestry. With GridVista, CTC can turn every inch of transmission into a smart sensor.

Why it matters: GridVista gives CTC grid operators an accurate and reliable view of what’s happening across the entire line — based on actual, real-time data from the entire length of the conductor, not point estimates from a static model of line conditions or the occasional clamped-on sensor. This means they can significantly improve safety, manage costs, increase the line’s capacity to transmit power, and enhance reliability with more precise insights about events that might trigger an outage.

Learn from us: “This awareness allows for a grid that can truly sense its own health in real time and provide unprecedented awareness of conditions on the entire line. Whether that’s real time storm impacts, ice load, wind load, branches on the wire, or temperatures on or under the line. The GridVista system truly represents next generation capabilities. ” — J.D. Sitton, CEO, CTC Global

ID.me reduces risk while scaling past 160 million users

Who: ID.me is transforming digital identity security for the modern era, offering a single login that lets you easily prove you’re you across a wide range of platforms and wallets.

What they did: ID.me currently serves more than 160 million users, including as many as 40,000 at any time, so they can prove their identity online as easily as flashing their driver’s license in person. Over the last two years, ID.me migrated more than 50 terabytes of data across 15 database instances to Google Cloud with minimal downtime. They also introduced a two-tier architecture with Cloud SQL supporting its smaller and more standard services, while AlloyDB runs heavier workflows that form the backbone of the ID.me platform.

Why it matters: AlloyDB AI has allowed ID.me to scale its systems to handle 10X-20X of what was possible before — and at a lower price to boot. That responsiveness and reliability led the U.S. federal government to recognize ID.me for its role in preventing large-scale fraud within national systems.

Learn from us: "We’ve been able to scale both our infrastructure and trust. With a platform that’s faster, smarter, and built to handle portable identity at massive scale, we’re one step closer to our goal: a secure, digital way to prove who you are, wherever you need it, that works everywhere you need it." — Kevin Liu, Cloud Platform Architect, ID.me

Manhattan Associates powers more than a billion daily API calls

Who: Manhattan Associates is a global leader in supply chain and omnichannel commerce solutions, offering tools and platforms that reach more than 2 billion people across 20 billion consumer touchpoints.

What they did: Manhattan Associates modernized its Manhattan Active SaaS platform by migrating from legacy Oracle and DB2 systems to Google Cloud databases. Each capability of Manhattan Active now runs as an independent, containerized service orchestrated by Google Kubernetes Engine (GKE). Data flows through Pub/Sub into BigQuery for real-time analytics, while Cloud Logging and Cloud Monitoring deliver observability at scale.

Why it matters: With its new microservices-first design, Manhattan gained the agility to evolve faster and the confidence that mission-critical operations would remain resilient across regions. With Cloud SQL and BigQuery, the company now processes more than a billion daily API calls with average response times of less than 150 milliseconds. This evolution supports hundreds of thousands of monthly active users across tens of thousands of stores and distribution centers. The new platform also created the foundation for Manhattan’s Agentic AI suite, which includes prebuilt agents — like the Intelligent Store Manager and Labor Optimizer — that coordinate real-time decisions across store and distribution center operations.

Learn from us: "Operationally, the platform has become more elastic and efficient. The system automatically handles hundreds of thousands of scaling events per day, ensuring performance remains consistent during peak surges without expensive overprovisioning." — Narayana Reddy Kothapu, Senior Director, Manhattan Associates & Rajkumar Ramani, Technical Director, Manhattan Associates

New ways to migrate and scale Red Hat OpenShift on Google Cloud

Thu, 26 Mar 2026 16:00:00 +0000

Organizations running on-premises workloads often face a difficult choice when moving to the cloud: how to modernize without losing the architectural consistency their business depends on. If you use Red Hat OpenShift, you have a way to bridge these environments without the risk of a forced re-platforming. Google Cloud provides a secure, reliable, and high-performance home for OpenShift, offering global scale and unique cost-optimization integrations. Building on this strong foundation, today, we are announcing several updates to our work with Red Hat—including Google Cloud Cluster Services for Red Hat OpenShift, a new console experience, and the general availability of OpenShift Virtualization on OpenShift Dedicated—to help you run containerized and virtualized workloads on Google Cloud’s high-performance infrastructure.

Optimizing costs with Google Cloud infrastructure

Google Cloud's global infrastructure is built for performance, scale, and efficiency, providing a world-class foundation for your most critical applications. This same innovative infrastructure offers significant advantages when deploying Red Hat OpenShift. Hundreds of enterprise customers such as Deutsche Börse, Kohl’s, Salling Group, Amadeus or UPS run production workloads on Red Hat OpenShift on Google Cloud to take advantage of our reliability and security. By moving to Google Cloud, you can use built-in technology to reduce your total cost of ownership (TCO) and match your infrastructure precisely to your workload needs:

Custom machine types: You can tailor CPU and memory to your specific requirements, which helps you avoid paying for unused resources in pre-packaged instances.
Hyperdisk storage pools: You can scale performance and capacity separately, allowing you to optimize costs across many volumes at once.
Axion processors: You can use Google’s custom ARM-based processors to achieve improved price/ performance and better energy efficiency.

The financial impact of these tools is clear. A recent IDC study on the business value of migrating Red Hat OpenShift to Google Cloud found that organizations achieved an average annual benefit of $26.3 million. The majority of this benefit comes from infrastructure savings gained by using tools like custom machine types and Hyperdisk.

Introducing Google Cloud Cluster Services for Red Hat OpenShift

Cluster Services for OpenShift provides OpenShift-native integrations with core Google Cloud services to simplify your operations. We want to make it easier for you to adopt a cloud-native operating model while maintaining consistency with your existing architectures. For many customers with on-premises OpenShift footprints, moving workloads to the cloud is a balancing act; you need to satisfy deep application or corporate policy dependencies while trying to gain cloud efficiency.

Google Cloud Cluster Services for OpenShift allows for the selective adoption of managed services, meaning you don't have to choose an "all or nothing" approach. Critically, these services are supported with all editions of OpenShift available on Google Cloud. This is especially important for migrations where a self-managed OpenShift edition is the required target architecture, allowing you to leverage a cloud-native operating model even in complex scenarios.

These integrations are jointly engineered and supported by Red Hat and Google Cloud, providing a single point of accountability. The offering includes:

Managed service integrations: Right from the OpenShift interfaces that you are familiar with, you can use deep hooks into services like Hyperdisk, Filestore, Cloud Monitoring, Managed Prometheus, Secret Manager, Workload Identity Federation, Certificate Authority Service and Google Identity; which we have customized for OpenShift use cases, for example by enabling <1TB Filestore instance sizes or providing support in Managed Prometheus for ingesting OpenTelemetry metrics.
Middleware and integration layer: We provide a robust stack including plugins and extensions for industry standards like CSI drivers, OpenTelemetry, cert-manager and Secret Store CSI to ensure an OpenShift-native integration between your platform and the underlying cloud services.

To keep your environment healthy, we are also introducing configuration validation through Google Cloud Workload Manager. This offering automatically checks that your integrations with managed services follow best practices and alerts you to any issues. It also highlights opportunities for your clusters to modernize with usage of managed services.

Creating clusters in the Google Cloud Console

Getting started with OpenShift should be intuitive. We have integrated OpenShift directly into the Google Cloud Console to provide a guided experience. You can now create new clusters through a simplified interface, making it faster to deploy your environment on our platform.

OpenShift experience on Google Cloud Console eases cluster deployment by checking for cluster creation prerequisites and redirecting to Red Hat Hybrid Console to complete cluster creation:

Running VMs and containers together with Red Hat OpenShift Virtualization

We are also announcing the general availability of Red Hat OpenShift Virtualization on Red Hat OpenShift Dedicated on Google Cloud. A capability of OpenShift, this feature allows you to run and manage virtual machines (VMs) alongside containers on the same platform. By using Google Cloud bare metal instances and Hyperdisk, you can migrate your VMs from legacy virtualization solutions into a modern Kubernetes-based control plane. Additionally, OpenShift Virtualization on Google Cloud provides the utmost level of flexibility with bare metal access, and hypervisor-level configurability, including ability to set custom overcommit settings that are just right for your workloads to help optimize your costs. With OpenShift Virtualization on Google Cloud you unify your infrastructure management and move toward the cloud at your own pace.

Get started

Read the Google Cloud Cluster Services for OpenShift documentation to learn more about its different components, and their availability.
Visit OpenShift on Google Cloud Console to explore the new cluster creation experience.
Read the OpenShift Virtualization documentation to learn how it can help you modernize your VM workloads.

Google Cloud and NVIDIA expand AI innovation across industries at GTC 2026

Mon, 16 Mar 2026 16:00:00 +0000

The era of agentic AI is fundamentally changing enterprise infrastructure needs. As organizations build systems capable of dynamic reasoning and autonomous execution, the underlying infrastructure must evolve as well. Scaling these agentic workloads alongside massive mixture-of-experts (MoE) architectures demands a deeply optimized co-engineered stack.

To meet these demands, we’ve built the Google Cloud AI Hypercomputer, an AI-optimized infrastructure as a service, that integrates performance-optimized hardware, leading software, open frameworks, and flexible consumption models into a single, cohesive system to deliver ultra-low latency, high-throughput, and cost-effective inference. To give our customers even more options within this integrated architecture, we are expanding our partnership with NVIDIA.

This week at NVIDIA GTC 2026, Google Cloud and NVIDIA are expanding our partnership with a wave of new announcements, showcasing a co-engineered AI infrastructure foundation:

Infrastructure and hardware

Strong momentum for Google Cloud G4 VMs, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition
Preview of flexible, fractional G4 VMs using NVIDIA vGPU technology — a first in the industry for NVIDIA RTX PRO 6000 Blackwell Server Edition
Upcoming support for NVIDIA Vera Rubin NVL72 Platform

Software and platform

NVIDIA Dynamo integration with GKE Inference Gateway
Enhanced NVIDIA support across Vertex AI Training and Model Garden

Ecosystem

Kaggle competition for NVIDIA Nemotron on G4 VMs
Launch of a dedicated public sector AI startup accelerator program

Let’s take a closer look at the announcements.

Accelerating AI workloads with G4 VMs

G4 VMs, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, are built to power a diverse spectrum of high-performance workloads — from advanced spatial computing to complete AI development lifecycles. For instance, companies like Otto Group One.O and WPP use the G4 to run physically accurate simulations and real-time 3D rendering at scale.

Beyond simulation, the G4 also shines in model fine-tuning and inference, particularly for models ranging from 30B to more than 100B parameters. By leveraging 4-bit floating point (FP4) precision and Google’s peer-to-peer (P2P) communication, customers are achieving higher throughput for model serving and considerable latency reductions, enabling a new class of real-time, multimodal AI agents and highly responsive generative AI applications.

Here are some examples of how customers are already leveraging the performance and efficiency of G4 VMs to accelerate their most demanding workloads:

“Google Cloud’s G4 VMs give us the scalable GPU backbone we need to push billions of miles of photorealistic simulation through our pipeline. The 4x lift in throughput means our ML teams can iterate faster, train on richer data, and validate edge cases long before our models ever see the real world.” – Sony Mohapatra, Director, AI/ML Engineering, General Motors

“Now with G4 VMs powered by NVIDIA Blackwell, we're pushing our multimodal models even further — faster inference, better reliability, instant replies across languages. The goal stays the same: making voice agents that work at enterprise scale without compromise. We are excited to keep building together and see what our customers deploy with this.” – Mati Staniszewski, Cofounder, ElevenLabs

“Google Cloud G4 VMs provide the computational backbone for our Robotic Coordination Layer, allowing us to synchronize autonomous fleets across our logistics centers with millisecond precision. By simulating complex warehouse environments in a high-fidelity digital twin, we can optimize our entire supply chain virtually before a single robot moves on the floor.” – Dr. Stefan Borsutzky, CEO of Otto Group One.O

“After transitioning to G4 VMs, we achieved a 50% reduction in processing latency and 6x increase in throughput just by updating our Terraform scripts. It’s rare to get that kind of performance boost for our core workloads without adding any operational overhead.” – Alfonso Acosta, Head of Engineering, Imgix

Introducing fractional G4 VMs

We are excited to announce the preview of fractional G4 VMs, providing a highly efficient and cost-effective entry point for AI and graphics workloads. These new configurations, using NVIDIA virtual GPU (vGPU) technology, allow you to leverage the power of the NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs in flexible, smaller increments, so you can right-size your infrastructure to match the specific demands of your applications.

“Enterprises need unprecedented flexibility to scale complex, agentic AI workloads. With Google Cloud, we’re introducing fractional G4 VMs powered by NVIDIA RTX PRO 6000 to let customers right‑size GPU capacity and maximize ROI. Together with our co‑engineered stack – from NVIDIA NeMo on Vertex AI to NVIDIA Dynamo with GKE – we’re delivering an open, high‑performance platform for next‑generation reasoning and MoE models.” – Ian Buck, VP / General Manager, Hyperscale and HPC, NVIDIA

By providing more granular access to advanced hardware, fractional G4 VMs let you optimize resource allocation and reduce overhead without sacrificing performance. You can now select from additional GPU slice sizes for your specific needs:

1/2 GPU: Ideal for more intensive tasks such as LLM inference, robotics sensor simulation, and high-fidelity 3D rendering.
1/4 GPU: Optimized for mainstream workloads, including mid-range creative design, video transcoding, and real-time data visualization.
1/8 GPU: Great for lightweight applications such as remote desktops, productivity tools, and entry-level streaming services.

These flexible G4 size portfolio let you:

Right-size infrastructure: Precisely match GPU capacity to application demands, ranging from lightweight remote desktops to intensive data processing.
Maximize cost efficiency: Lower operational overhead by utilizing — and paying for — only the fractional GPU resources you need for specific tasks.
Scale diverse workloads: Power a broad spectrum of innovation, from high-fidelity creative design and streaming to complex robotics simulations and real-time inference.

These fractional G4 VMs can be managed by Google Kubernetes Engine (GKE), allowing developers to use advanced container binpacking to achieve even higher price-performance and resource utilization. When managed through Dynamic Workload Scheduler, you can set fallback priorities for fractional slices. This significantly improves obtainability by allowing the scheduler to automatically find available GPU configurations for each workload.

“The G4 vGPU’s flexible sizing allows us to precisely tailor compute resources to the scale of each molecular simulation, ensuring maximum efficiency across our drug discovery pipeline. This granular control means our researchers can seamlessly pivot between smaller workflows and massive parallel processing without being constrained by fixed hardware configurations.” – Shane Brauner, EVP, CIO, Schrödinger

Scaling AI Hypercomputer with NVIDIA Vera Rubin NVL72

Building on our deep engineering partnership with NVIDIA, we’re proud to support the successor to NVIDIA Blackwell architecture, the recently announced NVIDIA Vera Rubin platform. We plan to be among the first cloud providers to offer NVIDIA Vera Rubin NVL72 rack-scale systems in the second half of 2026, integrating them into our AI Hypercomputer architecture to empower the next generation of reasoning and agentic AI.

Delivering efficiency across the AI infrastructure stack

As part of our commitment to a fully open ecosystem, we are excited to announce the integration of Dynamo and GKE Inference Gateway. This integration provides a modular, open-source control plane across the application layer and the hardware. By combining Dynamo with Inference Gateway on GKE, teams can tailor their infrastructure to their exact needs, allowing them to extract the maximum ROI from accelerators, accelerate time-to-market for new AI models, and future-proof their deployments.

You can learn to maximize performance for massive MoE architectures through new advanced scaling recipes for A4X VMs (powered by NVIDIA GB200 NVL72 and Dynamo). These configurations show how to overcome memory and interconnect bottlenecks when running AI inference workloads on AI Hypercomputer.

We are also enhancing resource obtainability through the Dynamic Workload Scheduler, with Calendar Mode and Flex Start for A4X and A4X Max (powered by NVIDIA GB300 NVL72), as well as new Flex Start support for G4 VMs. Dynamic Workload Scheduler lets you reserve the precise capacity that you need, or use flexible start windows.

Snap, a long-time Google Cloud customer, achieved significant cost savings by migrating two of its primary data processing pipelines to Google Cloud G2 VMs powered by NVIDIA L4 Tensor Core GPUs. This was made possible by leveraging Spark on GKE alongside NVIDIA’s new cuDF libraries, which automated the optimization of its shuffle-heavy workloads for optimal GPU efficiency. Learn more at GTC session S81678.

Advancing Vertex AI training and Model Garden

We are meeting the demands of next-generation AI with two major infrastructure advancements to Vertex AI training clusters. First, support for A4X VM domains lets you leverage Vertex AI’s managed infrastructure and framework capabilities for massive-scale training on NVIDIA GB200 NVL72 rack-scale systems. To ensure these intensive workloads remain uninterrupted, new hardware resiliency capabilities let you apply configurable, proactive fault detection scans, which identify and mitigate potential hardware issues before they can disrupt critical “hero” training runs. These capabilities enable higher goodput and helps ensure that multi-week training jobs stay on track without costly restarts.

“We are setting a new standard for the agentic enterprise — delivering highly capable, consistent, accurate, and responsive AI agents with Google and NVIDIA. By leveraging Vertex AI training clusters on NVIDIA GB200 NVL72 to power our Agentforce 360 Platform, we’ve eliminated infrastructure bottlenecks to keep our GPUs fully saturated. This high-performance, resilient architecture allows our researchers to focus on innovation at scale, driving substantial gains for our most complex reasoning workloads.” - Silvio Savarese, Chief Scientist, Salesforce

At the same time, we continue to broaden Vertex AI Model Garden with support for NVIDIA’s Nemotron 3 family of open models. These include the Nemotron 3 Nano, featuring one-click deployment to simplify integration into private VPCs. We’ve also expanded our catalog to include the NVIDIA Nemotron 3 Super 120B model for immediate access to high-performance, large-scale reasoning. To maximize the value of these models, we’ve integrated NVIDIA’s latest performance libraries directly into Vertex AI to optimize popular open-source models on NVIDIA TensorRT-LLM.

To enable the community to get hands-on with NVIDIA Nemotron on Google Cloud, we are also launching the NVIDIA Nemotron model reasoning challenge on Kaggle, powered by G4 VMs. The competition invites the community to improve Nemotron 3 Nano’s reasoning accuracy on a new benchmark using techniques such as prompting, synthetic data generation, data curation, and fine-tuning – all running on cost-efficient G4 infrastructure so participants can iterate quickly and share their methods with the broader ecosystem. To learn more and register, visit the Kaggle competition page.

Empowering public sector AI startups

To foster continued innovation within the ecosystem, Google Public Sector and NVIDIA are launching an AI startup accelerator program. This year-long initiative will support a select cohort of AI-focused Independent Software Vendors (ISVs) building solutions for the public sector.

Participants gain dual access to both NVIDIA Inception and Google Cloud’s ISV accelerator resources. Kicking off at GTC and continuing through Google Cloud Next, this joint program will equip emerging technology leaders with the co-engineered infrastructure, technical guidance, and go-to-market support required to scale mission-critical public sector applications. To learn more about the program, please complete the interest form. Additional cohorts will be selected and announced in the future.

Co-engineering collaboration powers every layer of the AI stack

The transition to complex, agentic AI demands more than just raw compute. It requires a fully optimized, co-engineered stack. By integrating flexible hardware like fractional G4 instances and the upcoming Vera Rubin platform into our AI Hypercomputer architecture, and pairing it with deep software co-engineering, we provide the scale, resilience, and efficiency you need to turn your most ambitious AI visions into reality.

Coming to GTC? Stop by booth #513 to learn more and talk to our team. And you can always learn more about our collaboration with NVIDIA at cloud.google.com/NVIDIA.

Fueling the autonomous network: Google Cloud and DigitalRoute simplify data readiness for AI

Wed, 04 Mar 2026 08:00:00 +0000

To achieve Level 4 and Level 5 autonomous network operations (ANO), the telecommunications sector is increasingly turning to AI to build self-healing, self-optimizing networks capable of predictive maintenance.

However there is a significant roadblock: AI is only as good as the data fueling it. While the industry fixates on AI agents, large language models (LLMs), and AIOps, the underlying data remains messy and fragmented. You can’t build a valid AI model on broken data.

Today, we’re announcing a new collaboration with data usage and monetization platform provider DigitalRoute to solve one of the industry’s biggest hurdles: data readiness. We’re launching reusable data pipelines that transform raw, chaotic network noise into high-quality, “AI-ready” data, enabling the next generation of autonomous agents.

Moving from "data swamps" to digital twins

Before an AI agent can resolve a network anomaly, it must first understand the environment. In a modern telecom network, this is a massive engineering challenge for four reasons:

Format disparity: Network data arrives in dozens of proprietary formats (e.g., ASN.1, XML, CSV, Binary) that can vary by vendor and domain. AI can't read these without a translator.
Volume and velocity: 5G networks generate billions of billing and event data records daily. Processing this in real time with ultra-low latency can crush legacy mediation systems.
Schema evolution: Every time a network function is upgraded or a new 5G feature is rolled out, the underlying data structure changes. This usually "breaks" traditional data systems.
Correlation complexity: A single customer session is often fragmented across multiple network elements. Without stitching these fragments together, AI lacks the context to understand the actual customer experience.

The result is a "data swamp" — massive amounts of unrefined information that AI models cannot use effectively.

The solution: A cloud-native architecture for data readiness

To address this, we’ve architected a solution with DigitalRoute that bridges legacy telecom protocols with cloud-native intelligence. By running DigitalRoute’s Usage Engine Private Edition on Google Kubernetes Engine (GKE), we’ve created a reusable pipeline that scales dynamically across edge and cloud locations.

This architecture provides three distinct advantages:

Normalization at the edge and core: We decode complex binary messages into a unified common data model and filter out irrelevant "noise" before it hits storage. This helps ensure downstream AI is decoupled from vendor-specific constraints while significantly reducing data egress costs.
The "dual-path" strategy: Once normalized, the data flows into two specialized environments:

For real-time operations: Data is pushed to Spanner to build a "network digital twin." This allows AI agents to traverse network topology over time to perform a quick root cause analysis.
For long-term analytics: Historical data flows into BigQuery. Because BigQuery is serverless and scales instantly, it allows operators to analyze petabytes of network traffic without managing infrastructure, feeding 'data products' directly into Vertex AI for rapid model training.

Contextualized subscriber traces: Through our Network and Subscriber Trace Analysis (NSTA) solution, we align messages from different nodes based on timestamps and identifiers. This builds a coherent, chronological session flow, giving AI agents the end-to-end context of the subscriber’s journey.

This architecture ensures that whether an AI agent needs to react to a fiber cut in near real-time with Spanner or predict a capacity crunch next month with BigQuery and Vertex AI, it is always consuming consistent, high-quality data.

From data to outcome: Enabling autonomous agents

This partnership is about enabling production-grade AI use cases. By establishing these reusable pipelines, we provide the foundation for:

Rapid anomaly detection: Agents can detect subtle deviations in network performance because the baseline data is consistent and noise-free.
Automated root cause analysis: When a failure occurs, agents can correlate radio access network (RAN) issues with core trace data instantly to pinpoint the fix.
Predictive maintenance: Using historical data in BigQuery, operators can train Graph Neural Networks (GNN) to predict capacity bottlenecks days in advance.

Accelerate your autonomous network mission

Data readiness is the difference between a "science project" and a production-grade autonomous network. By standardizing the ingestion layer today, telecoms can build the essential foundation for the AI-driven networks of tomorrow.

Ready to start?

Contact Google Cloud Sales to schedule a Data Readiness Workshop and explore our reference architecture for Autonomous Network Operations.
Reach out to DigitalRoute to learn how their Usage Engine on GKE can solve your specific format disparity and volume challenges.

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The rise of the autonomous network: How GraphML is redefining telecom operations

Wed, 04 Mar 2026 08:00:00 +0000

The complexity of modern telecommunications is exploding. Communication Service Providers (CSPs) are no longer managing static, isolated networks; they are orchestrating massive, multi-layer ecosystems that span 5G radio access, transport fiber, edge compute, and centralized cloud cores.

To manage this complexity, CSPs are building autonomous networks — self-managing telecommunications networks that use AI, machine learning, and closed-loop automation to self-configure, self-optimize, self-heal, and self-secure with minimal human intervention. According to Google Cloud’s autonomous networks framework, a critical building block for an autonomous network is a high-fidelity, real-time digital twin. You can then use advanced graph machine learning (GraphML) such as graph neural networks (GNNs) on the network digital twin to analyze, predict and proactively remediate potential problems on the live network.

This approach is not new at Google. It is the same architectural philosophy we use to manage our planet-scale network and power complex systems like Waymo’s self-driving car technology. GraphML enables this by understanding the geometry of the world around it, and the relationships of the objects in it, enabling it to make safe, autonomous decisions. Today, through a strategic collaboration with GraphML-based AIOPs solution provider NetAI, we’re bringing this same relationship-aware intelligence to the telecom industry. At Mobile World Congress (MWC) 2026, Spanish telecommunications provider MasOrange is showcasing a Proof of Concept (PoC) that explores this concept as a way to drive fully managed AIOps.

The digital twin foundation: Modeling your network

The first step in building an autonomous network is to move from a static inventory to a dynamic digital twin.

Traditional operational models rely on fragmented data lakes, where topology is often just another table. In our framework, we use Spanner Graph to build a dynamic digital twin of the network, explicitly modeling live network relations and topology. This twin captures billions of dependencies — mapping exactly how a specific optical transponder supports a particular IP interface, for example, which in turn supports a customer’s 5G slice.

This graph structure is not just for visualization; it provides the topological context as input for advanced GNN-based AI models.

GNNs vs. traditional ML

For years, the industry applied traditional machine learning (ML) to network operations, often with mixed results. To understand why GNNs are the superior choice for autonomous networks, let’s look at how they treat data compared to traditional methods.

Standard ML algorithms, such as random forests and basic deep learning, generally interpret network data as simplified, flat vectors or disconnected time-series sequences. This perspective results in models that:

Are topology-blind: Standard models do not inherently understand network topology. They don't "know" that Router A is physically connected to Switch B, meaning they miss the one of the most important features of a network failure: propagation.
Are symptom-based: Traditional ML models excel at detecting that a metric has spiked (anomaly detection) but struggle to understand why.
Detect correlation, not causality: These models rely on statistical correlation. If CPU usage spikes at the same time as packet loss, they assume a link. However, in a complex network, correlation is often coincidental or a downstream symptom of a distant failure.

GNNs are fundamentally different because they are structure-aware. This enables:

Native topology processing: GNNs ingest the network graph directly. They don't just look at the data on the nodes; they process the connections between them.
Deterministic reasoning: Instead of guessing based on statistics, GNNs use message passing. If a fiber cut occurs, the model mathematically propagates that "fault signal" along the known physical paths in the digital twin to predict exactly which upper-layer services will fail.
Precise anomaly detection: By understanding relationships between neighbors, a GNN can distinguish between a local anomaly (one chaotic router) and a structural anomaly (a sub-graph failure), drastically reducing false positives.

NetAI GNN on Google Autonomous Network stack

The technology stack: Google tf-GNN and NetAI

The MasOrange PoC is an exploration of GraphML, an integrated stack that combines Google’s infrastructure with NetAI’s domain expertise.

Google tf-GNN Library: Google developed and open-sourced TensorFlow GNN (tf-GNN), a production-tested library designed to build GNNs at massive scale. This is the same class of modeling Google uses to analyze complex graph structures in its own products, enabling developers to easily work with graph data and build large-scale GNN models.
NetAI fine-tuning: NetAI explored these foundational tf-GNN models and, in collaboration with Google, developed advanced GNN models that are significantly more tailored to the telecom use case. For example, the fine-tuned models understand specific telco behaviors such as distinguishing between BGP session flaps, optical signal degradation, and congestion-induced latency.
Managed AIOps: NetAI wraps these models in a fully managed platform that handles the end-to-end lifecycle, from automated network discovery and graph construction to the final root cause analysis.

Executive perspectives

"As we scale our operations, the ability to pinpoint root causes across millions of interconnected components is no longer optional. Partnering with Google Cloud and NetAI on this GraphML-driven approach allows us to explore and transform our observability into a proactive engine for service reliability." - Roberto González Librán, Head of Observability and Automation, MasOrange

"Our autonomous networks framework is designed to handle the intense data demands of modern telcos. By integrating GraphML capabilities with partners like NetAI, we are providing CSPs with the deterministic reasoning they need to run truly autonomous, self-healing networks." - Muninder Sambi, VP & GM of Networking, Google Cloud

Want to learn more?

The transition to autonomous networks requires more than just better monitoring; it requires a fundamental shift in how we model and reason about our infrastructure.

Ready to explore GraphML? Talk to your Google Cloud account team or contact NetAI to learn how GNN-powered digital twins can transform your network operations.

Scaling the autonomous network: Introducing the Data Steward and Core Network Agents

Wed, 04 Mar 2026 08:00:00 +0000

Last year, Google Cloud unveiled the Autonomous Network Operations framework, a comprehensive blueprint designed to help Communication Service Providers (CSPs) transition from manual management to zero-touch operations. As part of that launch, we introduced a RAN AI agent as a Proof-of-Value (PoV) asset, demonstrating how generative AI could autonomously optimize radio access networks (RAN).

Today, we’re announcing the evolution of our framework. Google Cloud. In partnership with Future Connections, we’re expanding the framework's capabilities to move beyond isolated tasks toward agentic AI that reasons and executes across the entire telecom stack. We’re introducing two critical innovations integrated directly into the umbrella: the Autonomous Data Steward and the Core Network VoLTE Agent, which New Zealand telecom provider One NZ has deployed into its operations.

"When we launched the Autonomous Network Operations Framework, our goal was to provide a blueprint for the future of telecom. Today, we are solidifying that framework by developing and launching critical new agents under the Autonomous Network umbrella. One NZ is deploying the Core Network VoLTE Agent, which is a testament to this progress, demonstrating how we are empowering operators to move beyond isolated use cases and truly operationalize AI at scale." - Muninder Sambi, VP & GM of Products, Google Distributed Cloud

The industry bottleneck: Why agents struggle to scale

While the industry has successfully deployed individual agents, scaling to a fully autonomous ecosystem has revealed a fundamental infrastructure challenge: data access is broken. Currently, every organization developing a new AI agent faces the same hurdles:

Data silos: Data is locked in rigid schemas and scattered across domains. For example, RAN and Core in BigQuery or Probes in Cloud Storage.
Semantic complexity: Without semantic context, data scientists can spend months deciphering cryptic vendor-specific counters (e.g., "pmCounterNumber_487") rather than building models.
Access issues: Each new agent requires a custom integration project, making rapid scaling challenging.

To solve this, we must first activate the data infrastructure’s intelligence. This requires a dual-layered approach: an intelligent agent to govern the data foundation and specialized agents to act upon it.

Expanding the framework: The Autonomous Data Steward

The Autonomous Data Steward is a Gemini-powered agent that serves as the intelligent backbone of the framework. It doesn’t just store data; it dynamically provisions it on demand for other agents to use.

Built on Google Cloud, the Steward utilizes a "zero-copy" architecture. Using Dataplex Universal Catalog, it stores metadata pointers rather than duplicating massive datasets. This approach reduces storage costs by up to 70% while helping ensure agents can access real-time telemetry.

The Autonomous Data Steward further automates the DataOps lifecycle by leveraging Google’s advanced reasoning models to bridge the gap between human intent and technical execution. This enables:

Semantic understanding: When a user or agent asks for data (e.g., "cell drop rate"), Gemini interprets the natural language intent and maps it to specific telecom counters using Dataplex Universal Catalog.
Dynamic ETL creation: If the dataset doesn't exist, the Steward autonomously writes and executes Python code (via Dataproc) to extract, clean, and join the data — reducing weeks of engineering to minutes.
Cross-domain unification: The agent can generate federated queries that join RAN performance, Core session failures, and Probe errors into a single view, transparently uniting isolated domains.

The Core Network (VoLTE) Agent

We are moving from reactive monitoring to self-healing networks. OSS remediation agents don’t just alert humans to problems; they diagnose and solve them. By breaking down data silos, we are building a cognitive layer that turns months of engineering expertise into milliseconds of automated action.

The IMS Quality Agent (VoLTE Agent) is our specialized core network specialist. Built on the foundation of the Autonomous Data Steward, it provides excellent voice service in three ways:

Real-time monitoring: It continuously tracks critical metrics like Call Setup Success Rates, Mean Opinion Scores (MOS), and CSCF performance.
Intelligent root cause analysis: Powered by Gemini’s multimodal reasoning, the agent correlates complex signaling data (SIP/Diameter) with Core performance to instantly identify issues like CSCF overload.
Autonomous recommendations: The agent recommends corrective actions, such as adjusting call routing or rebalancing load.

When the VoLTE Agent detects a dropped call, it queries the data to instantly fetch correlated IMS signaling and probe captures — data that previously lived in disconnected systems.

One NZ is already deploying this agent to proactively manage voice quality, moving beyond manual maintenance toward a fully autonomous network.

"We're excited to partner with Future Connections and Google Cloud, using Gemini's advanced capabilities to deploy agentic workflows into our network. This is another meaningful progress step on our journey towards fully autonomous network operations." - Kieran Byrne, CTO, One NZ

Accelerating the journey to autonomous operations

These launches represent an exciting leap forward in the framework. By integrating the infrastructure intelligence of the Autonomous Data Steward agent with the domain-specific logic of the VoLTE Agent into the Autonomous Network Operations framework, we’re delivering:

For operations teams: A critical MTTR accelerator, slashing the time required to identify and resolve voice service issues. This helps teams move from detection to resolution in a fraction of the time, maintaining strong network performance.
For data teams: A deployment accelerator, collapsing the time between model development and live production. This allows data teams to bypass traditional bottlenecks of data operations and realize the value of new ML deployments almost instantly.

Together, we’ve open-sourced these core methodologies, allowing operators to build and customize their own agentic workflows.

Ready to get started? If you’re interested in gaining early access to the Autonomous Data Steward or the VoLTE Agent, or want to learn more about One NZ’s deployment of the agent, please reach out to your Google Account Team or Future Connections today.

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From network APIs to network AI agents — building the agentic future of connectivity with Nokia

Tue, 03 Mar 2026 08:00:00 +0000

The telecommunications industry is entering its most significant shift yet: the agentic era. We’re moving beyond siloed automation tools toward a fully agentic ecosystem that can automate entire workflows.

At Mobile World Congress (MWC) Barcelona this week, we’re announcing the integration of Nokia’s Network as Code (NaC) platform with Google Cloud’s optimized agentic AI stack, to transform from network APIs into a fully agentic ecosystem. This delivers AI agents to telecommunications companies that can autonomously observe, program, and optimize the network through natural language and goal-oriented task automation.

The three pillars of the AI-network evolution

To meet the demands of the agentic era, the relationship between AI and the network is shifting from a "connected" model to a "deeply integrated" one. This evolution is unfolding across three distinct fronts:

The autonomous network: The network is transitioning from a manually managed utility to an autonomously orchestrated system with AI embedded directly into its control and data planes. By integrating intelligence into every phase — from initial build-out and planning to real-time fault detection and root cause analysis — the network becomes a self-healing fabric. This transition prepares the infrastructure for the programmable, high-fidelity data needs of the agentic era, ensuring that the network can anticipate and scale for demand before it impacts the enterprise.
Networks optimized for AI: As AI agents take on the "digital chores" of our lives, networks must be architected for heavy uplink data and low-latency "time-to-first-token" requirements. To make technology feel like magic rather than work, mobile networks must become hyper-dynamic, proactively negotiating with the nearest compute nodes like Google Distributed Cloud (GDC) to process tasks at the edge. This localized orchestration manages traffic and compute latency in the background, ensuring that AI assistants respond instantly without the user ever noticing a lag.
AI agents programming the network: While programmable networks have long promised to unlock value for enterprises and telcos, agentic AI is the catalyst that finally makes this accessible by transforming the developer experience. By exposing capabilities from the RAN to the core through a unified API framework, AI agents can now translate natural language prompts into complex, intent-driven network configurations. This shift enables autonomous systems to coordinate tasks and exchange data between agents, allowing the network to dynamically adapt to the specific needs of an application in real-time.

Nokia’s Network as Code platform — now connecting over 70 partners and 20+ network APIs — is becoming agent-enabled through integration with Gemini models and Google Cloud’s agentic framework. By leveraging standardized interaction protocols like A2A and MCP, these specialized agents move beyond simple automation to active reasoning — allowing them to interpret intent and manage complex network tasks autonomously.

The technical bridge: How Nokia and Google Cloud integrate

The shift from simple APIs to network AI agents is powered by a three-layer integration that connects Google Cloud’s intelligence to Nokia’s network expertise:

The exposure layer (Nokia Network as Code): This platform abstracts complex 5G core and RAN functions into standardized, Northbound APIs (CAMARA/GSMA Open Gateway). By accessing these Nokia APIs via Google Cloud Marketplace, developers can tap into global mobile networks and utilize the Network as Code portal to gain standardized access to network functions without the need to navigate underlying technologies.
The intelligence layer (Gemini with Network as Code): We are using Gemini to move from "calls" to "intents." Using MCP, Network as Code AI agents can autonomously identify and utilize network tools such as Quality of Service (QoS) or location verification to achieve specific business goals.
The interaction layer (agent-to-agent): Through the A2A protocol, a business’s agent (e.g., a logistics coordinator) can communicate a goal to a network agent. The network agent then translates that goal into network-specific instructions, orchestrating the network in real-time without manual coding.

Extensibility via the Agent Development Kit (ADK)

To enable a truly bespoke enterprise ecosystem, organizations can use the ADK to extend these capabilities. The ADK allows developers to build and deploy custom multi-agent systems that integrate proprietary business logic with Nokia’s network intelligence. This modularity ensures that specialized business agents can autonomously negotiate network resources to meet unique mission requirements, securely bridging the gap between internal enterprise data and external network capabilities.

From programmable network to intent-driven enterprise

The true power of this integration lies in how it bridges the "API gap." While Network as Code provides the essential programmable foundation, the addition of Agentic AI allows for a zero-code user experience. Developers no longer need to master low-level telco protocols; they simply define the business intent, and the agentic framework handles the underlying code execution.

The agent-enabled enterprise in action

By sensing real-time conditions — like a drop in mission-critical connectivity — the agentic ecosystem can autonomously resolve issues or provision resources before a service is impacted:

Autonomous logistics: A fleet management agent can request optimized connectivity for a future drone mission. The Network as Code agent automatically ensures coverage, quality, and priority throughout the flight path.
Enhanced security: An enterprise device management agent can ask a Network as Code agent to flag any customer subscription, device, or location anomalies in real-time, instantly hardening the security posture of mobile assets.
High-precision monitoring: A security agent can request a dedicated network slice to support multi-modal AI inference from mobile device feeds, ensuring the bandwidth is there exactly when the data starts flowing.

Why this matters for the telco ecosystem

This partnership bridges the gap between the complex world of network standards and the fast-moving world of cloud developers. For telco providers, it provides a clear path to transform the network from a cost center into an innovation engine. By making network capabilities discoverable and actionable within the Google Cloud Marketplace, telcos can finally monetize their 5G investments at scale.

“The network is no longer a bottleneck or a black box — it is a programmable, intelligent partner in our AI journey. With an intuitive intent-driven interface built into Nokia’s Network as Code, developing and deploying innovative services that leverage the network intelligence is possible at a much quicker pace, reducing time-to-revenue significantly.” - Vladimir Liulka, CEO, Blocksport

“Unlocking a seamless, intuitive developer experience is critical to the growth of monetizable services that leverage the full power of programmable networks and network APIs. Agentic AI framework, with agents providing the developer interface, provides the critical infrastructure to enable this experience. Nokia’s Network as Code platform, which is already integrated with Deutsche Telekom Network APIs, is a pioneer in enabling the transformation for programmable networks to the agentic world.” - Dr. Chathurangi Wickramasinghe, Senior Vice President, Magenta Business API, Deutsche Telekom

Experience the future at MWC

The shift to building the agentic future of connectivity is happening now. We invite you to join Nokia and Google Cloud at MWC to see these live use cases and discover how the Network as Code platform is turning the complexity of the network into a competitive advantage for the agentic era.

From framework to scale: Accelerating autonomous networks at MWC 26

Mon, 02 Mar 2026 08:05:00 +0000

Last year, we unveiled our Autonomous Network Operations framework — a blueprint for Communication Service Providers (CSPs) to move beyond siloed automation toward self-healing, "zero-touch" networks. Today, we’re pivoting from networks that merely use AI for insights to intelligent agents capable of sensing, reasoning, and taking autonomous action.

As we head into Mobile World Congress (MWC) Barcelona, we’re demonstrating how this shift is becoming a reality. By embedding AI into the heart of operations, companies like Deutsche Telekom and Vodafone are reducing operational complexity and turning connectivity from a utility into a value-creating engine.

Product innovation: The engine of the agentic telco

A key goal of the industry is to achieve Level 4 to 5 autonomy: a network that identifies, diagnoses, and fixes its own problems without human intervention. To do this, the underlying data platform must be as dynamic as the network itself. Over the last year, we’ve evolved our Cloud Spanner Graph and Vertex AI to handle the "dual nature" of telcos: the need for high-speed, real-time response for alarm correlation, combined with deep, historical pattern detection.

Today, we’re refining our platform to support these autonomous operations through:

The network digital twin: The network digital twin has evolved from a static map into a dynamic, temporal graph that represents the network’s live physical and logical state. It captures real-time performance and fault conditions while allowing agents to query historical states — such as the network’s appearance five hours or days ago — to perform instant, accurate root-cause analysis.
Unified graph data layer: We’re breaking down the silos between operational and analytical data. By leveraging Spanner Graph for digital twins and federated graph analytics through BigQuery, telcos can interoperate between real-time updates and deep historical analysis without complex, slow ETL (Extract, Transform, Load) processes.
Real-time predictions with GNN: Operators can now train Graph Neural Networks (GNNs) on their network digital twin data in Vertex AI. They can then use the trained GNN models, along with Spanner’s ML.PREDICT capability and the real-time data in the network digital twin, to move from monitoring to predicting, mathematically tracking how a failure might propagate, and resolving it before it impacts subscribers.

Solution updates: Accelerating time-to-value

One of the biggest hurdles to achieving Level 4 to 5 autonomy is manual delays caused by disconnected legacy systems. We’re launching new tools to replace bottlenecks with a unified, automated system:

Open-source data foundation: To accelerate adoption, we’re releasing our telco data pipeline and data models source code on GitHub. CSPs can now implement unified industry-standard ontologies without manual schema mapping.
New telco agents: In partnership with FutureConnections, we’re launching two new proof-of-value agents:
- Data steward agent: An agentic workflow that automates data governance to ensure digital twins remain accurate.
- Autonomous network agents: Currently being trialed by One NZ, these agents manage voice core and OSS networks, moving beyond monitoring to active execution — like independently rerouting traffic or resetting network settings to restore call quality the moment a drop is detected.

Ecosystem momentum: Innovation in action

True autonomy requires a vibrant ecosystem. This year, we’re highlighting several key milestones:.

MasOrange & NetAI: We’re working with NetAI to deliver GraphML-based AIOps. Together, we’ve launched a pilot project with MasOrange that demonstrates how specialized partner models can run on Google Cloud’s AI stack to resolve network incidents while providing the confidence for autonomous action.
Nokia "Network as Code": We’re partnering with Nokia to make networks fully programmable by turning complex technical code into AI agents that understand everyday language. This allows telcos to simply ask the network to perform complex tasks — like prioritizing network resources for critical services like emergency response or remote healthcare — without needing any manual engineering.

Looking ahead

The agentic AI era is here. By embedding AI into the fabric of a telco’s network, we’re helping operators transform from connectivity utilities into intelligent service providers that continue to delight customers.

Join us at MWC: Visit booth #2H40 in Hall 2 to see these solutions in action, including live demonstrations of multi-agent systems, digital twins, and physical AI robots. You can also dive deeper into our approach by downloading our latest whitepaper.

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Cool stuff Google Cloud customers built, Feb. edition: Telco data reinvention; Golden State’s “G.O.A.T.T.”; John Lewis explores DORA

Thu, 26 Feb 2026 17:00:00 +0000

AI and cloud technology are reshaping every corner of every industry around the world. Without our customers, there would be no Google Cloud, as they are the ones building the future on our platform. In this regular round-up, we dive into some of the exciting projects redefining businesses, shaping industries, and creating new categories.

For our latest edition, we explore a new data approach for Vodafone and Fastweb; evaluating John Lewis Partnership’s developer platforms; the Golden State Warrior’s AI playbook; healthy, stable networks at Hackensack Meridian Health; and Ab Initio brings better context to data for AI.

Be sure to check back next year to see how more industry leaders and exciting startups are putting Google Cloud technologies to use. And if you haven’t already, please peruse our list of 1,001 real-world gen AI use cases from our customers.

Fastweb + Vodafone reimagined data workflows

Who: Following the acquisition of Vodafone Italy by Swisscom in 2025, these leading European telecom providers wanted to rethink how they serve customers and deliver timely, personalized experiences across mobile, broadband, and digital channels.

What they did: Both companies had already begun modernizing customer data workflows with BigQuery, but combining ecosystems exposed certain limits of the existing setup. In order to give every channel real-time access to accurate customer data, they implemented Spanner as a service and governance layer, delivering low-latency reads, horizontal scalability, high availability, and a fully managed environment with zero ops overhead. The team is also using Gemini to generate clear documentation directly from the code, which saves hours of manual work.

Why it matters: Using Spanner Graph allowed the organization to map lineage in a way that reflects how its platform actually works: which tables drive specific jobs, how transformations cascade, and where dependencies sit. Call centers now see more complete, up-to-date customer information, digital channels can rely on consistent data without custom integrations, and partners can access what they need with low latency through Apigee.

Learn from us: “Rebuilding our Customer 360 platform with Google Cloud services has already changed how Fastweb + Vodafone works. Workflow monitoring is simpler, pipelines are leaner, and real-time serving is now the norm. ” – Vincenzo Forciniti, IT AI Adoption & Platform Engineering Lead, Fastweb + Vodafone

John Lewis measures the value of its developer platform

Who: The John Lewis Partnership is a major UK retailer operating John Lewis department stores and Waitrose supermarkets. To power their digital transformation, they built the John Lewis Digital Platform (JLDP) to support dozens of product teams building high-quality software for johnlewis.com.

What they did: Moving beyond simple usage metrics, John Lewis developed a sophisticated, multi-stage approach to measuring the real value of their platform. They transitioned from initial speed-based metrics (like "Onboarding Lead Time") to a comprehensive model using DORA metrics and subjective engineer feedback via the DX platform. This included a custom "Technical Health" feature that uses small, automated jobs to monitor more than 35 health measures — such as Kubernetes best practices, security, and operational readiness — providing teams with real-time "traffic light" indicators of their service health.

Why it matters: By focusing on value rather than just activity, John Lewis ensured the platform was actually reducing friction for developers rather than just being a mandatory tool. Their automated Technical Health checks allow product teams to manage technical debt and security vulnerabilities proactively. This approach has decoupled centralized operations teams from individual services, leading to faster incident resolution (MTTR), fewer outages, and significant cost savings.

Learn from us: "Measurement is a journey, not a destination. Start by measuring something meaningful to your stakeholders, but be prepared to adapt as your platform evolves. The things that mattered when you were proving out the platform's viability are unlikely to be what are important several years later when your features are mature." – Alex Moss, Principal Platform Engineer, John Lewis Partnership

Hackensack Meridian Health de-risks network migration using VPC Flow Logs

Who: Hackensack Meridian Health is a leading not-for-profit healthcare organization and the largest hospital system in New Jersey. System reliability is a cornerstone value for HMH as they manage a vast network of hospitals, urgent care centers, and physician practices.

What they did: Preparing for a large-scale migration to a new Google Cloud network design, Hackensack Meridian Health used VPC Flow Logs and Flow Analyzer to eliminate the "black box" of hybrid traffic. By enabling logs on their Cloud Interconnect VLAN attachments, they captured granular telemetry — including source/destination IPs, ports, and protocols. They then exported this data to create a visual "who-is-talking-to-what" map. This allowed them to identify critical traffic patterns between on-premises data centers and specific Google Cloud regions, VPCs, and applications.

Why it matters: In a healthcare environment, even minor network disruptions can have major consequences. By mapping traffic proactively, Hudson Meridian Health pinpointed exactly which moments in the cutover carried the highest risk. This preparation allowed them to detect a migration issue in just three minutes and resolve it within five — a process that previously could have taken hours. Beyond migration, this level of visibility enables the organization to better manage capacity planning, cost attribution, and security compliance across their hybrid infrastructure.

Learn from us: "Getting a clear picture of our interconnect traffic always felt like a black box. Enabling VPC Flow Logs and feeding it into Flow Analyzer finally gave us the map we needed. Identifying those critical traffic flows before we changed any routes was key to de-risking the entire migration." — Randall Brokaw, Cloud Engineering Manager, Hackensack Meridian Health

The Golden State Warriors’ AI-powered back office

Who: The Golden State Warriors are one of the NBA’s most successful modern franchises. Behind their on-court wins are a specialized operations team who run what might be called organization’s "G.O.A.T.T." (Greatest of All-Time Technologies), a data and AI platform that helps drive game-time insights, trading decisions, and fan experience enhancements.

What they did: The Warriors transitioned from a "gut-feeling" culture to an "analytics-first" strategy by building an internal "digital brain" on Google Cloud. Using BigQuery and Gemini, the team now automates complex workflows that previously took hours, such as generating pre-game scouting reports. They use machine learning to run thousands of trade simulations that prioritize "team fit" over raw individual stats and employ computer vision to track the "shot quality" of every attempt in the NBA. On the business side, they built a content recommendation engine using the Discovery API to deliver personalized digital experiences to their global fan base.

Why it matters: This AI-driven approach narrows the decision tree for leadership, allowing them to focus human expertise on the most viable options. By automating the “science” of data processing, coaches and scouts have more time for the "art" of face-to-face training, planning, and player development. This integration has not only influenced on-court strategy — like the three-point revolution — but has also improved business efficiency, with employees now proactively bringing AI-driven ideas to the IT team rather than waiting for top-down mandates.

Learn from us: "You can never reach a point where either humans or machines are making all the decisions. The sweet spot is finding that middle ground where intuition and data converge on the same conclusion. Data helps us narrow our decision tree before we even start evaluating specific options." — Nick Manning, Senior Director of Consumer Products & Emerging Technology, Golden State Warriors

Ab Initio unlocks enterprise data for the agentic AI era

Who: Ab Initio is an enterprise software company specializing in high-volume data integration and governance. Their platform is trusted by large-scale organizations to manage complex data lifecycles across hybrid and multi-cloud environments.

What they did: To solve the challenge of grounding AI agents in accurate data, Ab Initio partnered with Google Cloud to integrate its data fabric with BigQuery, Dataplex Universal Catalog, and Gemini. They launched a suite of more than 500 metadata and data connectors that bridge the gap between legacy systems (like mainframes, COBOL, and SAS) and modern cloud environments. This integration provides field-level, end-to-end lineage, allowing Gemini to access well-documented, "AI-ready" data regardless of where it resides.

Why it matters: AI agents are only as effective as the data they can access. By using Ab Initio as a "neutral hub," enterprises can federate data from on-premises and multi-cloud sources into a single unified layer without moving the data itself. This provides the rich semantic context and lineage needed for Gemini to perform grounded, explainable reasoning. For businesses, this means faster transition from experimental AI to production-ready agentic workflows that are auditable, compliant, and capable of making complex, automated decisions.

Learn from us: "Agentic AI requires trusted, AI-ready data and metadata. Understanding the origin, quality, and meaning of information matters as much as the data itself. Gemini serves as a key component of the agentic layer, using this context to make decisions that are explainable and auditable." — Scott Studer, Head of Development, Ab Initio & Chai Pydimukkala, Data Governance, Sharing & Integration Product Lead, Google Cloud

Monitoring Google ADK agentic applications with Datadog LLM Observability

Fri, 23 Jan 2026 17:00:00 +0000

Google’s Agent Development Kit (ADK) gives you the building blocks to create powerful agentic systems. These multi-step agents can plan, loop, collaborate, and call tools dynamically to solve problems on their own. However, this flexibility also makes them unpredictable, leading to potential issues like incomplete outputs, unexpected costs, and security risks. To help you manage this complexity, Datadog LLM Observability now provides automatic instrumentation for systems built with ADK. This integration gives you the visibility to monitor agent behavior, track costs and errors, and optimize agents for response quality and safety through offline experimentation and online evaluation without extensive manual setup.

This is significant as agentic systems are complex, and interagent interactions and the non-deterministic nature of LLMs makes it difficult to predict responses.

Common risks when running these agents include:

Pace of change: New foundation models drop weekly and “best-practice” prompting patterns change just as fast. Teams must constantly evaluate new combinations.
Multi-agent handoffs: If one agent produces low-quality output, it can cascade downstream and cause other agents to make poor decisions.
Loops and retries: Planners can get stuck calling the same tool repeatedly, such as retrying a search query indefinitely, which causes latency spikes.
Hidden costs: A single misrouted planner step can multiply token usage or API calls, pushing costs over budget.
Safety and accuracy: LLM responses may contain hallucinations, sensitive data, or prompt injection attempts, risking security incidents and reduced customer trust.

Finally, ADK is just one of many agentic frameworks available on the market. Having to manually instrument it only adds another learning curve to an already tedious and error-prone process.

Trace agent decisions and unexpected behaviors

Datadog LLM Observability addresses these pains by automatically instrumenting and tracing your ADK agents, so you can start evaluating your agents offline and monitoring them in production in minutes — without code changes. This allows you to visualize every step and planner choice — from agent orchestration to tool calls — on a single trace timeline.

For example, if an agent selects an incorrect tool to respond to a user query, it can yield unexpected errors or inaccurate responses. You can use Datadog’s visualizations to pinpoint the exact step where the incorrect tool was selected, making troubleshooting easier and helping you reproduce the issue.

Monitor token usage and latency

Sudden increases in latency or cost are often a sign of trouble in agentic applications. Datadog lets you view token usage and latency per tool, branch, and workflow to identify where errors happened and how they affected downstream steps.

For example, if a planner agent retries a summarization tool five times, it can significantly increase latency. Datadog highlights these loops, showing you exactly how long they took and the associated cost impact.

Evaluate agent response quality and security

Operational performance metrics like latency are critical monitoring signals, but for a holistic view of how agentic applications are performing, teams also need to evaluate the semantic quality of the LLM and agentic responses. Datadog provides built-in evaluations to detect hallucinations, PII leaks, prompt injections, and unsafe responses.

You can also add custom evaluators, including LLM-as-a-judge evaluators, for domain-specific checks. For instance, if a retrieval agent fetches irrelevant documents that lead to off-topic answers, a custom evaluator can flag that trace as having low retrieval relevance.

Iterate quickly and confidently with experiments

When you roll out a new system prompt, you might notice spikes in latency or drifts in output consistency. Datadog allows you to replay production LLM calls in its Playground to test different models, prompts, or parameters to find the configurations that move you closer to your ideal behavior.

From there, you can run structured experiments to compare versions side-by-side using datasets built from real traffic to optimize operational and functional performance. Because every agent step is logged through ADK instrumentation, you have the full context you need to reproduce regressions and validate fixes before you deploy.

Get started with Datadog LLM Observability

Datadog LLM Observability simplifies monitoring and debugging for Google ADK systems, helping users debug agent operations, evaluate responses, iterate quickly, and validate changes before deploying them to production.

You can get started today with the latest version of the LLM Observability SDK, or start a free trial if you are new to Datadog.

For more information on how to debug agent operations and evaluate responses, view Datadog’s LLM Observability documentation.

How Palo Alto Networks built a multi tenant scalable Unified Data Platform

Fri, 16 Jan 2026 17:00:00 +0000

Enterprises across the world are processing significant amounts of data. Palo Alto Networks processes thousands of firewall logs, telemetry signals and threat events every second across its product portfolio. To support this scale, Palo Alto Networks had 30,000 individual data pipelines, each with its own operational load. And while this single tenant architecture model worked originally, it had recently started to slow innovation, limit further scale, and made onboarding new analytics use cases increasingly costly.

To support the next generation of security products, Palo Alto Networks partnered with Google Cloud to modernize their data processing landscape into a unified multi-tenant platform powered by Dataflow, Pub/Sub and BigQuery. This transformation became the foundation of Palo Alto Network's Unified Data Platform (UDP), which now processes billions of events every day with improved agility, simpler operations and meaningful cost efficiency.

The challenge: A single tenant architecture could not keep pace

Before migrating, Palo Alto Network’s data platform was built around a “one pipeline per tenant” model. Each tenant pipeline required its own configuration, troubleshooting, on-call rotations and capacity tuning. As Palo Alto Networks usage grew, so did the friction:

Brittle alerting and weekly operational overhead to support more than 30,000 pipelines that were processing a combined throughput of roughly 30 GB per second.
Slow deployment cycles made onboarding new tenants harder.
Significant compute resources were dedicated to each tenant, regardless of load.
Engineering time was spent managing infrastructure instead of building new analytics.

This model hindered operational agility and made it challenging to scale,as new product lines expanded and data volumes increased.

The transformation: Embracing a new architectural paradigm with Dataflow

The turning point came when the team recognized that Google Cloud Dataflow’s serverless auto scaling architecture could support a completely different operating model. Instead of maintaining thousands of individual pipelines, Palo Alto Networks could unify workloads into a multi-tenant system where resources are shared intelligently across tenants.

Several core capabilities made this possible:

1. The architectural shift
Dataflow allowed the team to move from “one job per tenant” to a “shared resource pool” that can handle multiple tenants within a single architecture. This shift dramatically simplified operations and unlocked new efficiencies.

2. Unlocking multi tenancy at scale
Dataflow’s autoscaling engine manages fluctuating workloads with ease, accommodating the unpredictable spikes that are common in cybersecurity environments. This eliminated the need for manual capacity planning.

3. Operational freedom
By using Flex Templates and Dataflow’s managed service model, the team transformed their CI/CD process from week-long deployment cycles into a single day workflow. Engineers no longer spend time managing infrastructure and can instead focus on analytics, threat detection and product innovation.

4. Unified execution
With all jobs running on a shared Dataflow based platform, the team gained flexibility to move workloads across real time and batch systems without maintaining different codebases.

5. Observability
With Dataflow, the team relies on built in logging and metrics to monitor pipeline health across both real time and batch workloads, providing clear visibility into performance without additional tooling. Dataflow exposes the full set of metrics required for on-call alerting, eliminating the need to build or maintain custom metrics in the PANW codebase. When alerts trigger, the Dataflow UI enables engineers to quickly identify performance bottlenecks and take corrective action.

Architecture overview

Unified Dataflow based real time pipeline powering Palo Alto Networks UDP

The impact: A meaningful shift in value, cost and engineering focus

The migration to Dataflow did not just modernize the old system. It fundamentally changed how the engineering teams work, delivering impact across several dimensions.

The economic win: By consolidating pipelines and relying on Dataflow’s autoscaling, Palo Alto Networks achieved around 30 percent compute cost savings. These savings were driven by a reduction in redundant pipelines, better utilization of shared resources and elimination of manual capacity tuning.
The platform win: The Unified Data Platform became the long term standard for real time data processing across the company. It provides a “Dataflow native” blueprint that is scalable, repeatable and ready to support new product lines without duplicating engineering effort.
The people win: With Dataflow handling operational complexity, engineers now focus on building new analytics features instead of managing infrastructure. This shift improved morale, accelerated delivery cycles and reduced alert fatigue.

“The real differentiator for us was Dataflow’s ability to handle true multi-tenancy at massive scale. Its autoscaling engine is sophisticated enough to manage resources across thousands of tenants in a single job, which was key to unlocking around 30 percent cost savings. We moved from a world of managing more than 30,000 jobs to just a handful & that has fundamentally changed how our team operates.” — Palo Alto Networks Engineering Team

Extending the model: Use cases beyond cybersecurity

The architectural pattern Palo Alto Networks adopted is broadly applicable to any organization dealing with multi-tenant real time data at scale. Examples include:

E-commerce: powering real time dashboards for thousands of merchants on a single marketplace
Gaming: processing telemetry signals from millions of players to update leaderboards and detect fraud
Fintech: monitoring transactions from hundreds of banks to flag suspicious behavior in real time
IoT and logistics: analyzing data from fleets of vehicles to optimize routing and maintenance schedules

The same principles of multi-tenancy, shared execution and autoscaling can accelerate efficiency across many industries.

Building a sustainable data future

By standardizing on Dataflow, Palo Alto Networks has laid the foundation for long term agility in their security analytics platform. The Unified Data Platform now serves as the cornerstone of their real time data strategy, helping them innovate faster and operate with greater economic efficiency.

Their journey highlights how a flexible high performance data processing engine like Dataflow can give enterprises the confidence to scale without increasing operational overhead. It also provides a reusable playbook for teams that want to modernize their real time architectures using Google Cloud.

To learn more about how you can modernize your data pipelines, visit the Dataflow product page.

Palo Alto Networks automates customer intelligence document creation with agentic design

Wed, 14 Jan 2026 17:00:00 +0000

For a global cybersecurity leader like Palo Alto Networks, a comprehensive understanding of each customer is critical for success. For every engagement the Palo Alto Networks pre-sales team has, the comprehensive understanding is centralized in an internal Document of Record (DOR), a vital asset that provides a 360-degree standardized view of the customer for sales and support teams.

The Challenge: A manual, time-intensive process

Historically, creating a DOR was a manual process that required significant effort from highly skilled employees.

The task involved:

Gathering data from Salesforce.
Searching through extensive internal knowledge bases spread across multiple systems.
Synthesizing the information into a structured document.

This process could take days, delaying the opportunity to be won and closed, while diverting valuable time from experts who could otherwise be focused on customer-facing strategic work. To address this inefficiency, Palo Alto Networks sought to automate the entire workflow using a sophisticated AI agent built on Google Cloud.

An automated, agent-driven workflow

Palo Alto Networks developed an AI agent using Google's open-source Agent Development Kit (ADK) to autonomously generate the DOR. The required DOR questions are answered by utilizing GCP resources like Vertex AI RAG Engine, Vertex AI Discovery Engine Search (Google’s Enterprise search platform). The agent is deployed on Vertex AI Agent Engine, a fully managed platform that provides the necessary scalability and reliability for this business-critical task, while providing out-of-box session and memory handling.

Deployment architecture

The system's architecture is composed of the following core elements:

Two AI Agent Engine endpoints: These agents, deployed on Vertex AI Agent Engine, serve as API endpoints. They process POST requests from the Salesforce portal and return results, leveraging the managed scalability of Agent Engine for a distributed approach.
Webserver (FastAPI on GKE): Built with FastAPI and hosted on Google Kubernetes Engine (GKE), this web server orchestrates the system. It initiates requests to the Agent Engine endpoints, validates their responses, and stores the processed data.
Vertex AI Rag Engine: This acts as a serving datastore, providing the two AI agents with access to documents and logs uploaded to Google Cloud Storage (GCS).

The automated process steps

The automated workflow is a seven-step process, orchestrated by a central webserver, that seamlessly integrates Salesforce, Google Cloud AI services, and internal data sources.

Initiation from Salesforce: The process begins when a request for a specific customer account is triggered within Salesforce. This request is sent to a FastAPI webserver hosted on Google Kubernetes Engine (GKE).
Metadata Retrieval and Question Preparation: The webserver receives the request, retrieves the relevant customer metadata from Salesforce, and prepares a predefined list of over 140 standard questions designed to build a comprehensive customer profile.
Parallel Processing for Efficiency: To ensure the AI agent can scale and to optimize for speed, the webserver sends the questions to the Vertex AI Agent Engine endpoint in batches of five. This multi-threaded approach allows the Agent Engine to horizontally scale and process multiple questions concurrently - leveraging the managed auto-scale nature of Agent Engine.
Retrieval-Augmented Generation (RAG) and Vertex Discovery Engine: Each question is enhanced and clarified for enriching relevant context, which is achieved by pre-processing using Gemini 2.5 flash. It is then sent to two different agents, which then query their respective knowledge bases. This service acts as the RAG engine, searching a vast corpus of internal company documents and logs that were uploaded as part of briefing, to find and return only the most relevant snippets of information, grounding the agent's responses in factual, company-approved data.
LLM-Powered Answer Synthesis: The data snippets retrieved by Vertex AI Search are passed to a Gemini model, which synthesizes the information into a high-quality, coherent answer.Each agent independently to answer the questions and assigns a relevance score to its respective answer. Relevance score here measures the proportion of the claims in the answer that are grounded in the facts . he system then reconciles these answers with respect to the relevance score, selecting the best response and storing it as the definitive truth before moving to the next question. In addition, a verification step is performed. This step assesses the groundedness of the claims made in the final answer and categorizes the final answer into low, medium, or high confidence, allowing the end user to make the final call .
Stateful Orchestration: The FastAPI webserver manages the entire operation, storing the results and maintaining the state of the running process. It tracks which questions have been answered and consolidates the final document.
Asynchronous Handoff to Salesforce: Once all questions are answered, the webserver publishes the completed DOR to a Cloud Pub/Sub topic. This creates a reliable, asynchronous handoff. A separate service consumes the message from the topic and writes the final document back into the appropriate record in Salesforce, completing the workflow.

The technology stack

This solution effectively combines Google Cloud's managed AI services with open-source frameworks:

Agent Development Kit (ADK): The open-source Python framework used to define the agent's complex logic, including the multi-step orchestration, state management, and integration with various services.
Vertex AI Agent Engine: The fully managed, serverless environment that hosts and executes the ADK-based agent, handling scaling, security, and operational overhead.
Vertex AI RAG Engine: For generating contextually grounded responses. The Engine is configured to use Vertex AI Search as its retrieval backend, efficiently pulling relevant information from our internal documents to inform the language model.
Gemini Models: Provides the advanced reasoning and language synthesis capabilities required to generate high-quality, human-readable answers from the retrieved data.
Cloud Pub/Sub: Functions as a durable messaging queue that decouples the agent from the final write-back process, increasing the overall resilience and reliability of the architecture.
Cloud Storage: Served as storage for the unstructured customer documents needed to answer the DOR questions.

Overcoming challenges

The journey to automate DOR creation with an AI agent was not without its hurdles. Several key challenges were encountered and successfully addressed, highlighting important architectural and deployment considerations for similar agentic AI solutions.

1. Agent context management and scaling:

Initially, the design involved passing all 140+ questions to the agent at once, expecting it to iterate and manage its progress. However, this approach led to significant memory overloads and "Out of Memory" (OOM) errors. The agent's internal context window, which grew with each check against its logic and the accumulation of answers, quickly became unmanageable.

The solution involved shifting the responsibility for state management to a FastAPI server acting as an orchestrator. Instead of receiving all questions upfront, the agent was designed to process questions one by one. The FastAPI server now maintains the overall context and the accumulating document, passing individual questions to the agent and storing the agent's responses. This compartmentalization of context dramatically improved the agent's stability and allowed for more efficient scaling.

2. Deployment architecture and resource management:

Determining the optimal deployment architecture for both the backend orchestrator (FastAPI server) and the agent on Vertex AI Agent Engine posed another challenge. Early experiments with deploying both components within a single Google Kubernetes Engine (GKE) cluster resulted in frequent pod crashes, primarily due to the agent's context and memory demands. The decision was made to decouple the FastAPI server from the agent's runtime. The FastAPI server is deployed as a standalone service on GKE, which then makes calls to the agent separately deployed on Vertex AI Agent Engine. This separation leverages Vertex AI Agent Engine's fully managed and scalable environment for the agent, while providing the flexibility of a custom backend orchestrator.

3. Performance optimization for LLM calls:

The nature of generating answers using Gemini models, involving multiple API calls for each of the 140+ questions, initially resulted in a lengthy runtime of approximately 2.5 hours per DOR. Recognizing that these calls were I/O-bound, the process was significantly optimized through parallelization. By implementing multi-threading within the FastAPI orchestrator, multiple Gemini calls could be executed concurrently. Vertex AI Agent Engine's horizontal scaling capabilities further supported this parallel execution. This architectural change drastically reduced the overall processing time, improving efficiency by a substantial margin.

Business outcomes

The implementation of this AI agent has delivered significant, measurable results for Palo Alto Networks:

Increased Efficiency: The time required to create a comprehensive DOR has been dramatically reduced.
Improved Consistency and Quality: By standardizing on a 140-question framework, every DOR now meets a uniform high standard of quality and completeness.
Enhanced Accuracy: Grounding the agent's answers in a trusted RAG system minimizes the risk of human error and ensures the information is drawn from the latest internal documentation.
Strategic Re-focus of Personnel: The automation of this task allows expert employees to dedicate more time to high-value activities like customer strategy and direct engagement.
Ability to understand the gaps in documentation, areas where the answers are weak or absent ensures the pre-sales teams coordinating the efforts can emphasize on those topics for a more complete understanding of the customer.

This use case demonstrates a practical and powerful application of agentic AI in the enterprise, showcasing how a combination of open-source frameworks and managed cloud services can solve complex business challenges and drive operational efficiency.

^{The team would like to thank Googlers Hugo Selbie (GSD AI Incubation team) and Casey Justus (Professional Services) for their support and technical leadership on agents and agent frameworks as well as their deep expertise in ADK and Agent Engine.}