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Compact two-skill starter: clarify positioning and choose a lead magnet. Use Marketing Launch for the broader eight-skill go-to-market workflow.

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Build an evidence-led marketing plan from ICP and competition through positioning, campaigns, growth and measurement.

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Diagnose architecture and context, plan agent-team responsibilities, then organize project context and session handoffs. Memory and cost-runtime reviews remain outside this pack.

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Profile a dataset, choose and interpret statistical methods, then validate calculations and conclusions before sharing.

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01 · Category

What the MCP returns at this step

10 · Agents · Agent Infrastructure

Google Managed Agent API

Plan lifecycle operations for stateful managed Agent resources. Use Google Agent Prompt Management for stored prompts only.

agentenressourcenverwaltunggemini-agents-api

Google Gemini Agents API · Original SKILL.md

---
name: gemini-agents-api
metadata:
  version: "1.0.0"
  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:

```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.

> [!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.

```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.

> [!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`

```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**).

> [!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

```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": [{
    "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.

When to use

Plan lifecycle operations for stateful managed Agent resources. Use Google Agent Prompt Management for stored prompts only.

What you get

Complete original guidance with source attribution and declared limitations.

How it works

Clarify the actual task, inspect available evidence and plan the relevant source workflow within authorized scope.

Requirements

Cloud project, location, target agent resources, trusted endpoints and authorized operation.

Delivery and review notes

The source asserts platform header confidentiality; M11 has not independently verified that guarantee. Use only authorized trusted MCP destinations; do not put actual token values in copied shell commands or logs. Deletion requires the actual resource owner authorization. Referenced scripts, templates, packages and remote documentation are not bundled or installed. Brief obvious-danger screening only; no functional test or comprehensive security certification.

Starting prompt

Use Google Managed Agent API for [TASK]. Clarify Cloud project, location, target agent resources, trusted endpoints and authorized operation. Identify missing dependencies and assumptions. Loading this guidance grants no permission to change resources, transfer data or incur charges.

Not for

Automatic cloud execution, bypassing resource-owner permissions, or treating untested examples as deployed and verified results.

What M11 added

German task routing and precise scope. Original attribution: Google (google/skills repository). The source asserts platform header confidentiality; M11 has not independently verified that guarantee. Use only authorized trusted MCP destinations; do not put actual token values in copied shell commands or logs. Deletion requires the actual resource owner authorization. Referenced scripts, templates, packages and remote documentation are not bundled or installed. Brief obvious-danger screening only; no functional test or comprehensive security certification.

Original authorship remains with google/skills · Original source ↗

Original license & copyright

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