Agent Platform overview

Gemini Enterprise Agent Platform is a unified platform to build, deploy, govern, and optimize enterprise-grade AI agents and model-based solutions. As an evolution of Agent Platform, it supports the complete AI lifecycle from accessing over 200 foundation models to deploying and managing your agents.

Agent Platform meets you where you are, with tools for all skill levels:

• Low-code development:
  • Agent Studio: Design agents and interact with models without code.
• Code-based development:
  • Colab Enterprise Notebooks: Perform code-based development, data analysis, and experimentation.
  • Agent Development Kit: Build sophisticated Agents capable of complex reasoning and tool use with this modular, model-agnostic framework.

To meet enterprise requirements, Agent Platform includes integrated security and governance. Agent Identity lets you grant granular permissions to agents. Agent Gateway, together with Model Armor, secures all agent interactions, enforces runtime policies, and helps protect against threats and ensure compliant operations.

Gemini Enterprise Agent Platform Components

The Gemini Enterprise Agent Platform is organized around four key pillars:

• Build
• Scale
• Govern
• Optimize

Build

• Agent Development Kit: A modular, model-agnostic framework for building and deploying complex AI agents.
• Agent Studio: A low-code visual canvas for designing, prototyping, and managing agent reasoning loops and workflows.
• Agent Garden: A library of prebuilt agents and templates to accelerate development.
• Model Garden: Access to Google's frontier models (like Gemini models), third-party, and open-source models.
• RAG Engine: Securely connects private enterprise data to LLMs to improve answer accuracy and reduce hallucinations.
• Vector Search: AI-native search engine for storing, searching, and managing data for AI applications.

Scale

• Scale agents with Agent Runtime: High-performance, scalable runtime environment for deploying and managing agents, supporting features like sub-second cold starts and long-running agents.
• Agent Platform Sessions: Manages stateful data and context within a single agent interaction.
• Agent Platform Memory Bank: Enables agents to have persistent memory and recall information across multiple sessions.
• Code Execution: Enables AI agents to generate and run Python code in a secure, sandboxed environment to perform calculations, data analysis, and other complex logic.

Govern

• Agent Registry: A centralized catalog for discovering, tracking, and managing all agents, tools, and MCP servers across the organization.
• Agent Identity: Provides a fully managed, unique identity for each agent, enabling secure access control and auditing.
• Agent Gateway: A central policy enforcement point to govern all agent tool calls, manage authentication, and apply security policies.
• Governance Policies: Including Content Protection and Semantic Governance to mitigate risks like data leakage and ensure compliance.
• AI threat and vulnerability scanning: Real-time threat detection and vulnerability scanning specific to agentic systems.

Optimize

• Agent evaluation Systematically assess agent quality with tools like Multi-Turn AutoRaters and Online Evaluation for live traffic.
• Simulate and evaluate agent behavior Generate synthetic test scenarios and simulate multi-turn user interactions with configurable personas to stress-test agent logic.
• Observability Comprehensive monitoring, logging, and tracing tools, including the Unified Trace Viewer, provide detailed visibility into agent reasoning and performance for effective debugging.
• Optimize agent prompts Programmatically refine agent system instructions and tool descriptions by analyzing failure patterns and proposing targeted updates.

How to use this documentation

The Agent Platform documentation is organized into sections to help you find the information you need. Use the navigation tabs to explore the different areas of the platform:

• Studio: Discover how to use Agent Studio for prompt design, model tuning, and other UI-based interactions with models.
• Agents: Explore how to build, deploy, and manage AI agents for enterprise use cases using Agent Platform's agent framework and tools.
• Models: Learn about the generative AI models available in Agent Platform, including Gemini models, and how to use them in your applications.
• Notebooks: Find information on using Colab Enterprise notebooks for code-based model development, data analysis, and experimentation.

What's next