Prototype to Production: Gemini Enterprise Agent Platform

Year: 2026 · ▶ Watch on YouTube

Jason Davenport (Technical Lead for Developer Experience) · Dave Elliott (Developer Advocacy & Engineering Manager, AI) · Addy Osmani (Director, Cloud AI)

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Segments (8)

  • 00:00:00 · Introduction — Jason Davenport
    • The host welcomes the audience and introduces his guests, Dave Elliott and Addy Osmani, to discuss AI agents.
  • 00:00:27 · Introducing Gemini Enterprise Agent Platform — Addy Osmani
    • Addy introduces the Gemini Enterprise Agent Platform as an end-to-end solution for building, scaling, governing, and optimizing agents, addressing the difficulty of moving from prototype to production.
  • 00:01:25 · The ‘Build’ Pillar: Agent Development Kit (ADK) — Dave Elliott
    • Dave explains that the Agent Development Kit (ADK) is the core framework for building agents, supporting languages like Python, Go, TypeScript, and Java.
  • 00:02:42 · The ‘Govern’ Pillar: Security and Traceability — Addy Osmani
    • Addy details the governance features, including a gateway, cryptographically generated identities, and a registry, which provide traceability and security for agent workflows.
  • 00:05:13 · The ‘Scale’ Pillar: Sessions and Memory — Dave Elliott
    • Dave and Addy discuss how features like Agent Sessions and Memory Bank (now GA) enable persistent, stateful, and long-running agents that are more reliable for enterprise use.
  • 00:07:42 · The ‘Optimize’ Pillar: Evaluation and Simulation — Dave Elliott
    • Dave introduces the new ‘Optimize’ pillar, which includes Agent Evaluation and Simulation to help ensure non-deterministic agents perform as expected.
  • 00:09:56 · Community Innovations and the Future of Developers — All
    • The speakers discuss innovative community projects using agents and how the developer’s role is evolving into a problem-solver who manages fleets of agents.
  • 00:21:00 · Wrap-up and Next Steps — All
    • The guests give final thoughts, mention the AI Agents Challenge for startups, and encourage viewers to explore the new platform and codelabs.

Products Announced (1)

  • 00:00:28 · Gemini Enterprise Agent Platform (Mix of New and GA components)
    • End-to-end platform for building, scaling, governing, and optimizing AI agents. · Agent Development Kit (ADK) for building agents in multiple languages. · Comprehensive governance and security features like Agent Identity, Gateway, and Anomaly Detection.
    • Available now, with a public repo for exploration.

Demos (2)

  • 00:13:19 ✓ · Brain-Computer Interface Agent — Dave Elliott
    • A description of a demo on the conference floor where a BCI headset reads a user’s brainwaves, and an agent suggests work tasks or breaks based on their emotional state.
  • 00:14:14 ✓ · 30 Days Project — Addy Osmani
    • A description of a project that uses agents to scan social media (Reddit, Twitter) and summarize viral trends from the last 30 days.

Notable Quotes (4)

  • 00:00:48 — Addy Osmani:

    It’s very easy to build a prototype. It is very, very difficult to turn that into something you can put in production reliably.

  • 00:01:14 — Dave Elliott:

    It’s an end-to-end platform for building those agents, scaling them, governing them, and optimizing them.

  • 00:16:39 — Addy Osmani:

    The history of software engineering is a history of a rising set of abstractions.

  • 00:19:46 — Dave Elliott:

    It’s about democratizing AI, and I think we’re really at that stage now in the last maybe six months where we have democratized AI.

Visual Signals

On-screen (6)

  • 00:00:06 · Lower third: 'Jason Davenport, Technical Lead for Developer Experience, Google Cloud'
    • Identifies the host and his role.
  • 00:00:15 · Banner: 'Google Cloud Next Live from Vegas'
    • Sets the context for the live broadcast.
  • 00:00:46 · Lower third: 'Addy Osmani, Director, Cloud AI, Google Cloud'
    • Identifies the first guest and his role.
  • 00:01:23 · Lower third: 'Dave Elliott, Developer Advocacy & Engineering Manager, AI'
    • Identifies the second guest and his role.
  • 00:04:30 · Slide: 'Gemini Enterprise Agent Platform'
    • Visually presents the architecture of the newly announced platform, broken down into Build, Scale, Govern, and Optimize pillars with their respective components.
  • 00:22:15 · Google logo
    • End of segment branding.

Stage (1)

  • 00:00:00 · Three speakers are seated at a branded ‘Google Cloud Next’ desk in a large conference hall, conducting a live interview.

Visual demos (1)

  • 00:04:30 · A full-screen architectural diagram of the Gemini Enterprise Agent Platform.
    • The slide details four main sections (Build, Scale, Govern, Optimize) and lists multiple sub-components under each, with many marked as ‘New’ or ‘GA’.

Key Topics

Gemini Enterprise Agent Platform · AI Agents · Agent Development Kit (ADK) · Productionizing AI · AI Governance · AI Observability · Long-running Agents · Memory Management in AI · Agent Evaluation · Developer Experience (DevX) for AI · Democratization of AI · Future of Software Development · Agent Security · Agent Simulation

Takeaways

  • Google has launched the Gemini Enterprise Agent Platform, an end-to-end solution to help developers move AI agent prototypes into reliable, production-grade applications.
  • The platform is structured around four pillars: Build (e.g., Agent Development Kit), Scale (e.g., Memory Bank), Govern (e.g., Agent Identity), and Optimize (e.g., Agent Evaluation).
  • A major focus is on governance and security, providing tools for traceability, unique agent identities, and sandboxing to manage the risks of autonomous agents.
  • The platform’s ‘Scale’ features, like the now GA Memory Bank, enable persistent, stateful, and long-running agents, which are critical for complex enterprise tasks.
  • The new ‘Optimize’ pillar provides tools for agent evaluation and simulation, which are essential for managing the non-deterministic nature of LLM-based systems.
  • The Agent Development Kit (ADK) simplifies the building process with a framework that supports Python, Go, TypeScript, and Java.
  • The overall trend is the democratization of AI, where complex tasks like model training and deployment are becoming accessible to a broader range of developers, not just ML specialists.
  • The role of the developer is shifting from pure coding to becoming a ‘problem-solver’ and ‘manager of agent fleets,’ focusing on high-level architecture and quality control.