From Prototype to Production: Building with Gemini Enterprise Agent Platform
Summary
This session, recorded live at Google Cloud Next '26, features a fireside-style conversation between host Jason Davenport, Dave Elliott (Director of Product Management), and Addy Osmani discussing Google's Gemini Enterprise Agent Platform — an end-to-end platform designed to take AI agents from prototype to production. The central thesis is that while building a prototype agent is trivially easy, deploying one reliably in an enterprise context requires solving four distinct challenges organized into the platform's pillars: Build, Scale, Govern, and Optimize.
On the Build pillar, the speakers highlight the Agent Development Kit (ADK) as the foundational framework, supporting Python, Go, TypeScript, and Java. ADK was announced at the previous year's Cloud Next and is positioned as the fastest path to getting an agent operational.
The Govern pillar introduces several new capabilities: an agent gateway, cryptographically generated agent identities, an agent registry, and anomaly detection. Osmani emphasizes that unique, cryptographically secure identities for agents solve a long-standing enterprise problem — providing an auditable trail of which agent took which action, with secure credentialed access to services rather than shared or fuzzy token reuse.
Memory and long-running agents dominate the Scale discussion. Memory Bank is now generally available, having shipped roughly six months prior. The system handles storage decisions autonomously, reducing the expertise required to implement memory management. Long-running agents — capable of operating for days or weeks without losing context — are described as a first-class feature in Gemini Enterprise and one of the 'hidden gems' of the platform.
The Optimize pillar is described as the newest and most cutting-edge area. Agent Eval addresses the non-deterministic nature of LLMs by providing guarantees that multi-agent orchestration workflows accomplish their goals across the full execution flow. This includes agent tracing (a graph of agent actions), dashboard-level observability across an enterprise agent fleet, and simulation capabilities. Agent sandboxes are highlighted as critical for limiting 'blast radius' — giving agents access to only the tools they need, preventing over-privileged autonomous actions.
The conversation closes with a broader discussion about the developer's evolving role. Elliott and Osmani both invoke Grady Booch's observation that 'the history of software engineering is a history of a rising set of abstractions,' arguing that developers will shift toward managing fleets of agents while remaining responsible for quality, architecture, and design principles. Machine learning is predicted to grow and accelerate, not shrink, as AI democratizes access and draws more people into research. Two community projects are cited: a brain-computer interface agent that reads emotional state to prioritize work, and '30 days,' an agent that surfaces viral and important AI content from social platforms over rolling 30-day windows. The team also announced an AI Agent Challenge for Startups at the event.
Key Takeaways
- •Prototype-to-production is the core unsolved problem in enterprise AI: the platform specifically targets the gap between a working demo and a reliable production deployment.
- •Give your agents cryptographically unique identities — shared tokens or fuzzy credentials are an enterprise security anti-pattern; auditable, credentialed agent identities are now table stakes.
- •Memory Bank is GA and self-managing: it decides what to store and retrieves it automatically, removing the need for deep memory-architecture expertise when building agents.
- •Long-running agents (days to weeks) are now a first-class feature, but require robust persistence — an agent that forgets its mission mid-run is a production liability.
- •Agent evaluation must account for non-determinism across the full multi-agent flow, not just individual LLM calls; end-to-end guarantees matter more than per-step accuracy.
- •Sandboxes are mandatory for autonomous agents with tool access — scope permissions tightly and limit blast radius before deploying any agent with write or financial access.
- •Observability (agent tracing, dashboards) is the confidence layer that makes long-running autonomous agents viable in production — build it in from the start, not as an afterthought.
- •The developer role is shifting to a higher level of abstraction: managing fleets of agents rather than writing every line of logic, but accountability for quality and architecture remains human.
- •Machine learning is accelerating, not shrinking — AI is democratizing the field and pulling more people into research, with synthetic data generation now trivially available.
- •ADK supports four languages (Python, Go, TypeScript, Java), making it accessible across most enterprise engineering stacks without forcing a language migration.
Transcript
Hello everyone. Great to be here live with our first folks of the afternoon here or I guess it's morning. I'm on a different time zone here than where we're at at Google Cloud Next. I'd like to welcome two great friends of mine starting from the left Dave Elliott. Worked a long time with Dave in a developer relations and then director of product management Adi Osmany. Adi and Dave great to have you here. Great to be here. All right. Gemini Enterprise Agent Platform. We talked about it. What is i...
