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# Google's Gemini Enterprise Agent Platform: what's new and how to use it

**[Collections](https://daily.dev/sources/collections)** · 3 min read · 3 upvotes · 0 comments

## Summary

Google has moved several Gemini Enterprise Agent Platform features to general availability, including Agent Runtime (up to 7-day continuous execution with autoscaling), Agent Memory Bank (persistent context across sessions), Agent Identity (native IAM with least-privilege and audit logs), Agent Gateway (centralized prompt injection and data leakage screening), and Agent Registry (org-wide agent catalog). An evaluation system with 20+ pre-built metrics co-developed with Google DeepMind is also GA, supporting both offline and live traffic monitoring with automated test case generation. Google published an end-to-end tutorial using an 'Industry Watch' agent that reconciles SEC filings against press releases, walking through all six lifecycle stages: setup, build, deploy, govern, evaluate, and publish.

## Content

Google has moved several Gemini Enterprise Agent Platform features to general availability, and published a detailed walkthrough showing how they fit together in practice. Here's what's shipping and what it looks like to actually build with it.

## What's now generally available

**Agent Runtime** lets agents run continuously for up to seven days, with managed autoscaling and persistent memory across long-running tasks.

**Agent Memory Bank** gives agents structured context retention — so an agent working through a multi-day task doesn't start from scratch each session.

**Agent Identity** is a native IAM type that enforces least-privilege access and produces non-repudiable audit logs. Agents get their own identity rather than borrowing a human user's credentials.

**Agent Gateway** is a centralized control point that screens for prompt injection and data leakage. It's where you enforce security policy without scattering that logic across individual agents.

**Agent Registry** is an organization-wide catalog of all deployed agents — useful once you have more than a handful running and need to track what exists and who owns it.

Google also announced **CodeMender**, a managed agent for automated vulnerability remediation in codebases.

Customers including AT&T, Best Buy, Commerzbank, Liberty Global, and WellSky are already using these features in production, particularly the governance and memory capabilities.

## Evaluations are also GA

The evaluation system lets you measure agent quality consistently from development through production, using the same metrics engine for both offline experiments and live traffic monitoring.

Out of the box you get 20+ pre-built metrics: Task Success, Tool Use Quality, Safety, Hallucination, Grounding, and others. The LLM-judge rubrics were co-developed with Google DeepMind and adapt to your specific use case. You can also write custom metrics in code or as prompts.

Test case generation is automated through case generators, user simulators, and environment simulators — which matters because building a good eval dataset by hand is usually the bottleneck. Evaluation artifacts are stored in Cloud Storage for auditability.

The system integrates with the Agent Platform SDK, `agents-cli`, ADK, and the Google Cloud console.

## Building an agent end to end: the Industry Watch example

Google published a tutorial that walks through all six stages of the agent development lifecycle using a concrete example: an agent called Industry Watch that reconciles SEC filings against press releases for semiconductor stocks.

The six stages are **setup, build, deploy, govern, evaluate, and publish** — and the whole thing runs without leaving the coding agent environment.

**Setup and build:** You scaffold an ADK agent using `agents-cli`. The Industry Watch agent pulls SEC filings and press releases, compares them, and flags discrepancies.

**Deploy:** The agent deploys to a managed autoscaling runtime with persistent memory via Agent Runtime and Agent Memory Bank.

**Govern:** Security is handled through least-privilege IAM via Agent Identity, plus Model Armor prompt injection screening through Agent Gateway.

**Evaluate:** You run grounded evaluations against curated test cases with deterministic pass/fail metrics — the same evaluation system described above, applied to this specific agent.

**Publish:** The agent gets published to a Gemini Enterprise app and registered in Agent Registry.

The tutorial is worth reading if you're trying to understand how these pieces connect in practice rather than just what each feature does in isolation. The governance and evaluation steps in particular tend to get skipped in demos, so seeing them treated as first-class stages is useful.

## Similar posts on daily.dev

- [Google Cloud Announces Gemini Enterprise Agent Platform](https://daily.dev/posts/google-cloud-announces-gemini-enterprise-agent-platform-b4ocuevnb) · C\# Corner · 0 upvotes · 0 comments
- [The new Gemini Enterprise: one platform for agent development](https://daily.dev/posts/the-new-gemini-enterprise-one-platform-for-agent-development-cekaoebmc) · Google Cloud · 0 upvotes · 0 comments

---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#gcp](https://daily.dev/tags/gcp), [#google-gemini](https://daily.dev/tags/google-gemini)

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