/gemini-agents-api

name: gemini-agents-api
metadata:
  category: AiAndMachineLearning
description: Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.

Gemini Enterprise Agent Platform - Managed Agents API Skill

This skill provides complete instructions, REST request endpoints, and JSON payload structures to programmatically manage **custom Agent resources** on the Gemini Enterprise Agent Platform (Agent Platform).

The **Managed Agents API** forms the **Control Plane** of the platform. It allows developers to provision, retrieve, update, and delete tailored, stateful agent containers equipped with system instructions, sandboxed files, custom skill registries, and local/remote tools. ---

1. Authentication & Setup

All REST requests to the Control Plane must include a Bearer token derived from Application Default Credentials (ADC), and target the production global endpoint.

1. Setup Environment Variables

Before running requests, set up the required project variables and access token:

export PROJECT_ID="your-project-id"
export LOCATION="global"
export ACCESS_TOKEN=$(gcloud auth print-access-token)

> [!IMPORTANT] > **API Location Support**: > The `LOCATION` environment variable must be set to a regional location where the Gemini Enterprise Agent Platform's **Managed Agents API** is actively supported (e.g., `global`, or other available regional endpoints).

2. Endpoint URL

The production Agents Control Plane endpoint is:

https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/agents

---

2. Programmatic Agent Management (Control Plane CRUD)

1. Create Agent (Long-Running Operation)

To create a new agent resource, issue a `POST` request with the custom configuration. You can mount remote files, folders, or skills directly from **Google Cloud Storage** buckets into the agent container's workspace. Creating an agent is a Long-Running Operation (LRO) that spawns an asynchronous job.

• **Method**: `POST`
• **Endpoint**: `https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents`

Request Payload

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json; charset=utf-8" \
  -d '{
    "id": "my-custom-agent",
    "base_agent": "antigravity-preview-05-2026",
    "description": "A professional agent configured with remote tools and mounted Cloud Storage directories.",
    "system_instruction": "You are a helpful, domain-expert assistant.",
    "tools": [
      {"type": "code_execution"},
      {"type": "filesystem"},
      {"type": "google_search"},
      {"type": "url_context"}
    ],
    "base_environment": {
      "type": "remote",
      "sources": [
        {
          "type": "gcs",
          "source": "gs://your-agent-bucket-name/skills",
          "target": "/.agent/skills"
        }
      ],
      "network": {
        "allowlist": [
          { "domain": "*" }
        ]
      }
    }
  }'

LRO Operations Response

Since agent provisioning takes a few moments, the endpoint immediately returns an operation tracking object:

{
  "name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.CreateAgentOperationMetadata",
    "genericMetadata": {
      "createTime": "2026-05-14T19:00:00.123456Z",
      "updateTime": "2026-05-14T19:00:01.654321Z"
    }
  }
}

[Advanced] Mount Skill Registry Resources

To mount skills directly from the Skill Registry service instead of Cloud Storage, replace the Cloud Storage source item in the payload:

"sources": [
  {
    "type": "skill_registry",
    "source": "projects/your-project-id/locations/global/skills/my-math-skill/revisions/123456789012",
    "target": "/.agent/skills"
  }
]

[Advanced] Configuring Model Context Protocol (MCP) Servers

To configure Third-Party MCP servers for an agent, add the server metadata directly under the `"tools"` parameter array inside the creation request. The platform securely routes tool execution requests to the external MCP server.

> [!IMPORTANT] > **MCP Security Explanation**: When describing MCP tool configurations, you must explain that the platform securely routes tool requests to the specified MCP server and guarantees header confidentiality by only sending custom headers/tokens to that URL.

"tools": [
  {
    "type": "mcp",
    "name": "my-mcp-server",
    "url": "https://mcp.yourcompany.com/api",
    "headers": {
      "Authorization": "Bearer YOUR_MCP_AUTH_TOKEN"
    }
  }
]

• **name**: A descriptive name for the MCP server.
• **url**: The endpoint URL of the external MCP server.
• **headers**: (Optional) Custom key-value pairs containing authentication tokens (e.g. API keys, bearer tokens) required to call the server. The platform guarantees that these headers are only sent to the specified MCP server URL.

> [!TIP] > **Overriding MCP at Interaction Time (Data Plane)**: > You can dynamically override or supply MCP tools directly when creating a conversation interaction (Data Plane) by passing `"type": "mcp_server"` inside the `"tools"` payload of `interactions.create`. Refer to the Interactions API documentation for details.

---

2. Polling the LRO Status

To track the status of agent creation and obtain the final ready resource, poll the operation URL returned in the `name` field of the creation response.

• **Method**: `GET`
• **Endpoint**: `https://aiplatform.googleapis.com/v1beta1/{OPERATION_NAME}`

curl -X GET "https://aiplatform.googleapis.com/v1bet