The Agentic Stack: Discover the Gemini Enterprise Agent Platform.

Google Cloud introduces the Gemini Enterprise Agent Platform, an agentic stack designed to help enterprises move from building single AI agents to managing thousands of them with complete governance. The platform enables specialization, orchestration, memory, and action capabilities, treating agents as digital workers with defined responsibilities and permissions.

Learn how to move from building single agents to managing a mission control of thousands of them – all with complete governance. The Agentic Stack: Why Managing Thousands of AI Agents Is the Next Enterprise Revolution For the past two years, the AI industry has been obsessed with one question: "How do we build better AI agents?" But we're now entering a new era where that question is no longer enough. The real challenge isn't creating one intelligent agent—it's managing thousands of them. Imagine an enterprise where every department has its own specialized AI: Now imagine these agents working together, sharing context, using enterprise data, and operating under strict governance. That's what the Agentic Stack is all about. It's not just another AI framework—it's a blueprint for building an enterprise powered by intelligent, autonomous systems. Most developers begin by building a single AI assistant. It can answer questions, summarize documents, or automate a workflow. That's a great starting point. But enterprises don't operate through a single workflow. They run hundreds of interconnected processes every day. Instead of one AI assistant, organizations need an ecosystem of specialized agents that collaborate to achieve business goals. Think of it like a modern company: The future of AI follows the same model. The Agentic Stack is the foundation for building, deploying, orchestrating, and governing AI agents at enterprise scale. Rather than treating AI as a chatbot, it treats agents as digital workers with clearly defined responsibilities. A complete Agentic Stack typically includes: Each agent has a focused responsibility instead of trying to solve every problem. Examples include: Specialization improves both reliability and performance. Real business problems often require multiple steps. A customer asking for a refund might trigger: Customer Agent ↓ Order Verification Agent ↓ Fraud Detection Agent ↓ Finance Agent ↓ Notification Agent No single agent should handle the entire process. Instead, orchestration ensures the right agent performs the right task at the right time. An AI agent without business context is like a new employee on their first day. To make informed decisions, agents need secure access to: Retrieval-Augmented Generation RAG helps agents retrieve relevant information instead of relying only on model memory. Enterprise agents create value when they take action. Instead of only generating text, they should be able to: An agent becomes truly useful when it can move from reasoning to execution. Modern AI agents shouldn't forget every conversation after a single interaction. Persistent memory enables them to: Context transforms isolated conversations into continuous collaboration. As organizations deploy hundreds—or even thousands—of AI agents, governance becomes the foundation of trust. Without governance, businesses risk: Enterprise AI requires guardrails, including: Every agent should have clearly defined permissions. A marketing agent should never access payroll records. A finance agent shouldn't modify engineering systems. Every decision should be traceable. Organizations need visibility into: Transparency is essential for compliance and debugging. Not every action should be fully autonomous. Critical tasks—such as financial transactions, legal approvals, or customer escalations—should include human review. The goal isn't to replace people. It's to automate routine work while preserving human oversight where it matters most. Imagine opening a dashboard that shows: This becomes the mission control center for enterprise AI. Just as Kubernetes transformed how we manage containers, the next generation of platforms will transform how we manage AI agents. The future isn't about launching one agent. It's about operating an entire workforce of them. Scaling AI agents isn't just a technical problem. Organizations need to address: Thousands of AI agents can generate significant inference costs. Smart orchestration determines: Efficiency becomes a competitive advantage. AI agents interact with sensitive systems. Protecting customer data requires: Security must be built into the architecture—not added later. Traditional monitoring tracks servers and APIs. Agentic systems also need to measure: Without observability, improving AI systems becomes guesswork. Enterprise software has evolved through distinct phases: The next generation of software won't simply help employees work faster. It will delegate meaningful work to autonomous, governed AI agents that collaborate across business functions. This isn't about replacing humans. It's about enabling people to focus on creativity, strategy, and innovation while AI handles repetitive, structured work at scale. The conversation around AI is rapidly shifting. Building a single AI chatbot is no longer enough. The organizations that gain the greatest advantage will be those that can orchestrate thousands of specialized agents, connect them to enterprise knowledge, govern their behavior, and continuously monitor their performance. The future belongs not to the company with the smartest individual agent—but to the one with the most effective Agentic Stack . The next enterprise operating system won't just run applications. It will coordinate an intelligent workforce of AI agents working together toward a common goal. The age of autonomous enterprises has begun.