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Agent Platform Endpoint Management

Free

Efficiently manage your AI model serving endpoints.

by google17.6k stars on google/skills
Updated Aug 10, 2026
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Free · Opens the source repo

What Agent Platform Endpoint Management does

The Agent Platform Endpoint Management skill is designed for developers and data scientists who need to manage serving endpoints on the Agent Platform. Endpoints act as stable URLs that facilitate online predictions for deployed AI models. This skill allows users to create, list, describe, update, and delete these endpoints, ensuring that they have the necessary infrastructure for their machine learning applications. It also provides troubleshooting capabilities for common issues such as permission errors and quota limits.

When using this skill, it is critical to follow the safety and confirmation tiers outlined in the documentation. Read-only commands such as listing and describing endpoints can be executed immediately, while mutating commands like creating or updating endpoints require interactive confirmation. This ensures that users have control over potentially impactful changes to their endpoint configurations. Additionally, the skill emphasizes the importance of correctly setting up the environment, including Google Cloud authentication and project configuration, before executing any commands.

This skill is particularly useful for teams working with AI models who need to manage their deployment infrastructure efficiently. It streamlines the process of endpoint management, allowing users to focus on model development and deployment rather than getting bogged down in configuration details. By providing clear commands and structured workflows, the skill enhances productivity and reduces the likelihood of errors during endpoint management.

However, it's important to note that this skill does not handle the deployment of models to endpoints or running model evaluations. Users will need to utilize other skills for those specific tasks. Overall, the Agent Platform Endpoint Management skill is a valuable tool for anyone looking to simplify their endpoint management on the Agent Platform.

When to use it

Use this skill when you need to create, list, describe, update, or delete serving endpoints for your AI models.

When not to use it

Do not use this skill for deploying models or running evaluations; it is solely focused on endpoint management.

What you can build with it

Creating a New Endpoint

Use this skill to create a new endpoint for deploying your AI models, ensuring you follow the confirmation process.

Listing Existing Endpoints

Quickly list all existing endpoints in your specified region to manage your AI model infrastructure effectively.

Troubleshooting Endpoint Issues

Utilize the troubleshooting section to resolve common errors related to permissions and resource availability.

How to install Agent Platform Endpoint Management

View source

1. Install with the skills CLI

npx skills add google/skills/agent-platform-endpoint-management --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by google

Agent Platform Endpoint Management

Overview

This skill provides procedural knowledge for managing Agent Platform Endpoints. Endpoints are logical serving hosts that provide a stable URL for online predictions. You must create an endpoint before you can deploy a model to it.

Safety & Confirmation Tiers (CRITICAL)

Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:

  1. Tier R: Read-only (list, describe, get)
    • No confirmation needed. Execute immediately to gather information.
  2. Tier M: Mutating & Reversible (create, update)
    • Requires interactive confirmation with 'Yes'/'No' options. The confirmation prompt MUST contain the exact, literal command string with all required flags (e.g. --region=us-central1, --display-name="...") — natural-language paraphrases are NOT sufficient.
    • Same-turn restriction: NEVER execute the command in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / approval.
  3. Tier D: Destructive & Irreversible (delete)
    • Requires explicit typed confirmation (e.g. "I confirm" or "Yes, delete it"). Ask for confirmation IMMEDIATELY — before any pre-flight checks (don't describe first, don't check if the endpoint is empty first).
    • Same-turn restriction: NEVER execute in the same turn as asking for typed confirmation. Wait for the user to reply in a new turn.

Phase 0: Environment Setup

CRITICAL: Before running any commands, you MUST ensure the environment is correctly initialized by following these steps:

  1. Google Cloud Authentication: Authenticate with your Google Cloud credentials and configure active Application Default Credentials (ADC) for Agent Platform access:

    gcloud auth login
    gcloud auth application-default login
    
  2. Set Project: Configure the active project for subsequent commands:

    gcloud config set project $PROJECT_ID
    
  3. Region: Always specify --region=$LOCATION_ID on each command below. Do NOT use global. Ask the user to specify the region if not provided.

1. Listing Endpoints (Tier R)

Use this command to discover existing endpoints in a specific region and retrieve their IDs. No confirmation is required.

gcloud ai endpoints list \
    --region=$LOCATION_ID

(Optional) For pagination, you MUST use --limit=$LIMIT to restrict the total number of returned endpoints. You can also append --page-size=$PAGE_SIZE to control API chunking, or --page-token=$PAGE_TOKEN for next pages.

[!IMPORTANT]

Always specify the --region. Do NOT use 'global'. Ask the user to specify if not provided.

2. Describing an Endpoint (Tier R)

Retrieve the full metadata for a specific endpoint. No confirmation is required.

gcloud ai endpoints describe $ENDPOINT_ID \
    --region=$LOCATION_ID

3. Creating an Endpoint (Tier M)

Create a new endpoint resource. The parent resource is the location. Action requires an inline confirmation card before proceeding.

gcloud ai endpoints create \
    --region=$LOCATION_ID \
    --display-name="my-endpoint"

[!IMPORTANT]

You MUST seek interactive confirmation first. Your confirmation prompt MUST show the literal command string. For example:

gcloud ai endpoints create --region=$LOCATION_ID --display-name="my-endpoint"

Or the exact flags. Do not execute this command in the same turn as proposing the confirmation.

4. Updating an Endpoint (Tier M)

Update endpoint metadata such as display name or labels. Action requires an inline confirmation card before proceeding.

gcloud ai endpoints update $ENDPOINT_ID \
    --region=$LOCATION_ID \
    --display-name="new-display-name"

Check if the endpoint exists first by either listing or describing the endpoint.

[!IMPORTANT]

You MUST seek interactive confirmation first. Your confirmation prompt MUST show the literal command string. For example:

gcloud ai endpoints update $ENDPOINT_ID --region=$LOCATION_ID --display-name="new-display-name"

Or the exact flags. CRITICAL: You are strictly prohibited from executing this command in the same turn as asking for confirmation. When you ask for confirmation, you MUST stop immediately and wait for the user to reply.

5. Deleting an Endpoint (Tier D)

Permanently delete an endpoint resource. Action requires explicit typed confirmation before proceeding.

gcloud ai endpoints delete $ENDPOINT_ID \
    --region=$LOCATION_ID

[!WARNING]

All models must be undeployed from the endpoint before it can be deleted. Do not run describe until AFTER you have received typed confirmation to delete.

6. Traffic Splitting (Tier M)

You can manage traffic split between different models deployed on the same endpoint during an update. Action requires an inline confirmation card before proceeding.

# Example: Deploying a model with a specific traffic split is usually done
# via 'gcloud ai endpoints deploy-model'.

Refer to the agent-platform-deploy skill for instructions on deploying and undeploying models.

Troubleshooting

  • 403 Permission Denied: Ensure aiplatform.admin or owner role is assigned.
  • Quota Exceeded: Verify the region's endpoint quota in the Cloud Console.
  • Resource Busy: If a deletion fails, check if models are still being undeployed.

Frequently asked questions about Agent Platform Endpoint Management

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