Gemini Enterprise Agent Platform

What is Gemini Enterprise Agent Platform?

Gemini Enterprise Agent Platform is Google Cloud’s platform for building, scaling, governing, and optimizing AI agents for enterprise use. It brings together the model selection, model building, and agent building capabilities from Vertex AI, and adds capabilities focused on agent integration, orchestration, DevOps, and security.

The platform is designed as a single place for technical teams to create agents that can be delivered to employees through the Gemini Enterprise app, while staying integrated with IT operations so teams can apply control, governance, and security as agent complexity grows.

Key Features

• Agent Studio (low-code interface): Provides a visual, low-code way to build agents, helping teams choose and configure the environment for an agent’s job.
• Agent Development Kit (code-first logic): Supports code-first development for building agent logic, using upgraded ADK capabilities alongside AI-native coding to help ship production-grade agents.
• Agent Runtime (re-engineered for production): Enables long-running agents that maintain state for days, supporting persistent context via Memory Bank.
• Centralized governance components: Uses Agent Identity, Agent Registry, and Agent Gateway to provide trackable identities and enterprise-grade guardrails across agents.
• Quality and visibility tooling: Includes Agent Simulation, Agent Evaluation, and Agent Observability, providing execution traces and a real-time view into agent reasoning to help verify goals are met.
• Model access via Model Garden: Offers access to more than 200 models, including first-party models such as Gemini 3.1 Pro, Gemini 3.1 Flash Image, and Lyria 3, plus open models like Gemma 4.
• Model flexibility with third-party options: Supports selecting third-party models such as Anthropic Claude Opus, Sonnet, and Haiku for the best fit to the task.

How to Use Gemini Enterprise Agent Platform

1. Open the platform in the Google Cloud console to explore Agent Platform features.
2. Build an agent using either Agent Studio (visual workflow) or Agent Development Kit (ADK) (code-first logic).
3. Run the agent in Agent Runtime so it can maintain state for long-running tasks and use Memory Bank for persistent context.
4. Add governance and access control using Agent Identity, Agent Registry, and Agent Gateway so agents operate under enterprise guardrails.
5. Validate and monitor performance with Agent Simulation, Agent Evaluation, and Agent Observability before and during deployment.
6. Deliver the agent to employees through the Gemini Enterprise app, while keeping tight integration with IT operations.

Use Cases

• Enterprise knowledge and project-intelligence agents: Build an agent that turns large internal datasets (for example, decades of project data) into real-time, actionable intelligence while using deterministic business rules alongside probabilistic reasoning.
• Healthcare scheduling and eligibility assistants: Create an agent that engages users to check eligibility, connect to clinicians, and schedule appointments, then scale via long-running execution and persistent context.
• Production agent lifecycle for complex multi-system workflows: Use the platform’s orchestration and DevOps-focused features to manage agents that interact across multiple systems where governance and reliability are required.
• Governed agent catalogs across teams: Apply centralized control with Agent Identity, Registry, and Gateway so multiple agents—built on the platform or sourced from partner ecosystems—remain trackable and operate within guardrails.
• Quality assurance using simulation and evaluation: Run simulations and evaluations to generate execution traces and inspect agent reasoning to confirm the agent reaches its goals.

FAQ

Is Gemini Enterprise Agent Platform the same as Vertex AI?

It is described as the evolution of Vertex AI: it combines Vertex AI’s model selection, model building, and agent building capabilities and then adds additional features for agent integration, DevOps, orchestration, and security.

Where do I start building agents?

The source states you can start by visiting Agent Platform in the Google Cloud console and exploring the new features.

Can I use models other than Google’s?

Yes. The platform provides access to more than 200 models via Model Garden, and it also supports selecting third-party models such as Anthropic Claude Opus, Sonnet, and Haiku.

How does the platform support long-running agents?

It uses a re-engineered Agent Runtime designed to support long-running agents that maintain state for days, with Memory Bank for persistent, long-term context.

How are governance and monitoring handled?

Governance is supported through Agent Identity, Agent Registry, and Agent Gateway, while quality and monitoring are supported through Agent Simulation, Agent Evaluation, and Agent Observability (including execution traces and a real-time view into agent reasoning).

Alternatives

• Build agents using a model + orchestration stack (general-purpose AI services): Instead of a dedicated agent platform, teams may combine model hosting, orchestration, and their own governance tooling; this can offer flexibility but typically requires more integration work.
• Vertex AI-based workflows without a dedicated agent platform layer: Organizations already using Vertex AI may build agent functionality directly, but the source indicates future Vertex AI services and roadmap evolutions will be delivered exclusively through the Agent Platform rather than as a standalone service.
• Custom enterprise agent solutions: Teams can develop bespoke agent infrastructure to meet governance and runtime needs; this shifts effort toward engineering, observability, and operational controls.
• Agent frameworks focused mainly on development: Some tooling focuses on creating agents but may not provide the same integrated combination of runtime state, centralized governance components, and evaluation/observability capabilities described here.