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:

bash

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:

http

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

bash

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:

json

{
  "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:

json

"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.

json

"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}

bash

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

json

{
  "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"

json

{
  "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}

bash

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:

json

{
  "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

bash

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:

json

{
  "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.

Method:
```
PATCH
```

Endpoint:

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

bash

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}

bash

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

json

{}

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).

To interact with your custom agent:

Obtain your agent's resource path name (e.g.,

projects/{PROJECT_ID}/locations/global/agents/{AGENT_ID}

Pass this resource path directly inside your data plane conversation requests under the agent
parameter.

Python Example

python

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

REST / curl Example

json

{
  "agent": "projects/your-project-id/locations/global/agents/my-custom-agent",
  "input": [{
    "role": "user",
    "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.

gemini-managed-agents-api

NPX Install

Tags

SKILL.md Content

Gemini Enterprise Agent Platform - Managed Agents API Skill

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

1. Setup Environment Variables

2. Endpoint URL

2. Programmatic Agent Management (Control Plane CRUD)

1. Create Agent (Long-Running Operation)

Request Payload

LRO Operations Response

[Advanced] Mount Skill Registry Resources

[Advanced] Configuring Model Context Protocol (MCP) Servers

2. Polling the LRO Status

In-Progress Response

Finished Success Response

3. Get Agent

Response Example

4. List Agents

Response Example

5. Update Agent (Patching Configuration)

6. Delete Agent

Response Example

3. Interacting with Custom Agents (Data Plane)

Python Example

REST / curl Example