Note: This project is part of the official Google Codelabs Integrate Gemini Enterprise Agents with Google Workspace and Integrate Vertex AI Agents with Google Workspace.

This sample contains a specialized Enterprise Agent built using the Google Agent Development Kit (ADK). This agent acts as an Enterprise AI Assistant by querying user's data corpus using the Vertex AI Search MCP toolset and sending Chat messages to DM spaces using a custom Function tool & Google Chat API.

1. Dynamic Vertex AI Serving Configs: The agent automatically discovers your project's default_collection engine and dynamically binds its queries to the default_serving_config.

2. Dynamic Authentication (ToolContext): The client (e.g. Gemini Enterprise app, Google Workspace add on) passes an authentication token in the session state (e.g., enterprise-ai_12345). This agent intercepts the ToolContext state and extracts the token at runtime using regex pattern matching (^enterprise-ai_\d+$) to securely execute calls using a Bearer token.

3. Graceful Timeouts: The McpToolset streaming components have been intentionally configured with an explicit 15-second timeout and sse_read_timeout to prevent the agent from hanging infinitely on backend network issues.

4. Google Chat Integration: The agent natively includes a send_direct_message tool powered by the google-apps-chat SDK. This allows the AI to immediately send direct messages to users inside Google Chat. It seamlessly reuses the same authentication token extracted from the ToolContext used for Vertex AI.

This agent is designed exclusively to be deployed as a backend without any active auth. It will not work successfully if tested locally via standard ADK run commands because it relies entirely on an external gateway to inject OAuth tokens into the ToolContext at runtime.

Deploy this agent directly to Vertex AI Agent Engines using the ADK CLI:

adk deploy agent_engine \
  --project=your-gcp-project-id \
  --region=us-central1 \
  --display_name="Enterprise AI" \
  enterprise_ai