Enterprise Email Agents on Google Vertex AI

Connect Vertex AI's Gemini models and Agent Builder to MultiMail for sending, reading, and managing email — with enterprise governance and human oversight.


Google Cloud's Vertex AI provides enterprise access to Gemini models along with Agent Builder for creating grounded agents with tool use and security controls. MultiMail gives Vertex AI agents the email infrastructure layer they need to send, receive, and manage email within the GCP ecosystem.

By integrating MultiMail with Vertex AI, enterprises get compliance-ready email capabilities for their AI agents. Vertex AI's IAM controls handle model access, and MultiMail's oversight modes handle send authorization. The default gated_send mode means your agent drafts emails but a human approves before delivery.

Connect Vertex AI to MultiMail by defining email functions using Vertex AI's function calling format, or by configuring email tools as extensions in Vertex AI Agent Builder.

Built for Vertex AI developers

Enterprise Compliance

Vertex AI's enterprise focus requires compliance-ready integrations. MultiMail's oversight modes and audit trails satisfy GCP enterprise customers' requirements for AI governance in email communications.

Gemini Function Calling

Vertex AI provides access to Gemini models with function calling. Define MultiMail email operations as function declarations with typed parameters that Gemini fills reliably.

Agent Builder Integration

Vertex AI Agent Builder supports custom tools and extensions. Configure MultiMail email operations as tools in your Agent Builder agent for managed, scalable email agent deployments.

Graduated Trust via Oversight Modes

Start with gated_send (agent composes, human approves) and progress to autonomous as trust builds. MultiMail's oversight modes align with enterprise rollout strategies for AI capabilities.

GCP Ecosystem Integration

MultiMail complements existing GCP services. Use Cloud Functions or Cloud Run for tool execution, Secret Manager for API key storage, and Cloud Logging for end-to-end observability.


Try it with your agent

No code, no dashboard. Paste this to your AI agent — it connects MultiMail, creates an inbox, and builds the flow for you.

1. Get MultiMail ready: read https://multimail.dev/llms.txt, follow its instructions to connect the MultiMail MCP server, create a free inbox for this agent, and set up a verified sending domain. 2. Wire MultiMail into Vertex AI: build or update my Vertex AI Agent Builder agent with Google ADK using an LlmAgent backed by a Gemini model on Vertex AI, then add MultiMail through ADK's McpToolset in the agent tools list using the MCP connection details from llms.txt so the agent can discover and call MultiMail tools. 3. Give the agent only these email abilities at first: check the MultiMail inbox, compose a draft reply, and send email from the verified sender. Use the actual discovered MultiMail MCP tool names and schemas; do not invent endpoints, headers, or APIs. 4. Send a test email: ask the agent to check its inbox, draft a short reply to one test message, show me the draft, and prepare it to send from the verified sender. 5. Run with human review: configure MultiMail oversight mode to gated_send before any send or schedule action, require my approval for the test email, and keep gated_send enabled until I explicitly switch to monitored or autonomous.

Step by step

1

Create a MultiMail Account and API Key

Sign up at multimail.dev, create a mailbox, and generate an API key. Store it in Google Secret Manager for secure access.

2

Install Dependencies

Install the Vertex AI SDK and requests library for calling the MultiMail API.

3

Initialize Vertex AI

Configure the Vertex AI SDK with your GCP project and region. Define email function declarations for Gemini's tool use.

4

Build the Agent

Create a GenerativeModel with email tools and implement the chat-based agent loop with function call handling.

5

Approve Pending Emails

If your mailbox uses gated_send mode (the default), review and approve pending emails in the MultiMail dashboard before they are delivered.


Common questions

Should I use Vertex AI's Gemini SDK or Agent Builder?
Use the Gemini SDK for custom agent implementations where you control the execution loop. Use Agent Builder for managed agents with built-in grounding, memory, and deployment features. Both approaches integrate with MultiMail's email tools.
How does Vertex AI's security model work with MultiMail?
Vertex AI uses GCP IAM for model access control and Secret Manager for credential storage. Store your MultiMail API key in Secret Manager and use service account permissions to access it. MultiMail's oversight modes add email-specific authorization on top of GCP's security.
What happens when my agent sends an email in gated_send mode?
In gated_send mode, the MultiMail API returns a success response with a pending status. The email is queued for human review in the MultiMail dashboard. Once approved, it is delivered. Your agent can check the status via the list_pending endpoint.
Can I use Vertex AI's grounding features with email agents?
Yes. Vertex AI's grounding can connect your agent to Google Search or custom data stores for context when composing emails. The agent retrieves relevant information through grounding and uses MultiMail to send context-rich emails.
Is there rate limiting on the MultiMail API?
Rate limits depend on your plan tier. The Starter (free) plan allows 200 emails per month, while paid plans range from 5,000 to 150,000. The API returns standard 429 responses when limits are reached, which your agent can handle with retry logic.

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