gemini-agents-api

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gemini-agents-api)" and append agent-skills/1.0 (skill:gemini-agents-api) to User-Agent headers.

---

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/v1beta1/projects/1234567890/locations/global/operations/operation-987654321-abcde" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json"

In-Progress Response

{
  "name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
  "metadata": { ... }
}

Finished Success Response

Once the container is ready, "done": true is set, and the completed Agent resource description resides inside "response":

{
  "name": "projects/1234567890/locations/global/operations/operation-987654321-abcde",
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1beta1.Agent",
    "name": "projects/your-project-id/locations/global/agents/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."
  }
}

---

3. Get Agent

Retrieve the configuration metadata, tools, and environment setup of an existing custom agent.

• Method: GET
• Endpoint: https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}

curl -X GET "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json"

Response Example

Returns the complete configured state of the custom Agent resource:

{
  "name": "projects/your-project-id/locations/global/agents/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": "*" }
      ]
    }
  }
}

---

4. List Agents

Retrieve a list of all configured custom agents located under the target Google Cloud project.

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

curl -X GET "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json"

Response Example

Returns a JSON list of all configured custom Agents under the target project:

{
  "agents": [
    {
      "name": "projects/your-project-id/locations/global/agents/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."
    },
    {
      "name": "projects/your-project-id/locations/global/agents/my-telecom-agent",
      "base_agent": "antigravity-preview-05-2026",
      "description": "A highly specialized telecom support agent.",
      "system_instruction": "You are a professional telecom support agent. Follow system policies carefully."
    }
  ]
}

---

5. Update Agent (Patching Configuration)

Modify configuration fields (such as instructions, descriptions, tools, or mounts) on a custom agent resource in place. You must specify the fields being updated using the update_mask query parameter.

Important

Update Mask Requirement: When demonstrating updates, you must always explicitly explain that the update_mask parameter is required when updating agent configurations to specify exactly which fields are being modified and avoid overwriting other configuration settings.

• Method: PATCH
• Endpoint: https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}?update_mask=system_instruction

curl -X PATCH "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent?update_mask=system_instruction" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-custom-agent",
    "system_instruction": "You are a highly specialized telecom support agent. Follow system policies carefully."
  }'

---

6. Delete Agent

Delete custom Agent resources when they are no longer needed to free up backend workspace containers.

• Method: DELETE
• Endpoint: https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/agents/{AGENT_ID}

curl -X DELETE "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/agents/my-custom-agent" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}"

Response Example

A successful deletion request returns an empty JSON response body with HTTP Status 200 OK:

{}

---

3. Interacting with Custom Agents (Data Plane)

Once you have programmatically created and provisioned your custom stateful agent using the Control Plane (this skill), you can execute multi-turn chat, tool execution, and streaming conversations with it using the Data Plane (Interactions API).

Important

Interactions Reference: When explaining or showing how to start conversations with a custom agent, you must always explicitly refer the user to the gemini-interactions-api skill for complete conversation and streaming options.

To interact with your custom agent:

1. Obtain your agent's resource path name (e.g., projects/{PROJECT_ID}/locations/global/agents/{AGENT_ID}).
2. Pass this resource path directly inside your data plane conversation requests under the agent parameter.

Python Example

interaction = client.interactions.create(
    agent="projects/your-project-id/locations/global/agents/my-custom-agent",
    input="Hello! Who are you?"
)

REST / curl Example

{
  "agent": "projects/your-project-id/locations/global/agents/my-custom-agent",
  "input": [{
    "type": "user_input",
    "content": [{"type": "text", "text": "Hello! Who are you?"}]
  }]
}

Refer to the gemini-interactions-api skill guide (../gemini-interactions-api/SKILL.md) for full instructions, Python and TS/JS code blocks, and streaming setups to run conversations with your provisioned agents.