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agent-platform-endpoint-management

Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.

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2026년 10월 8일 01:18
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name
agent-platform-endpoint-management
metadata
{"version":"1.0.1","category":"AiAndMachineLearning"}
description
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
# 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: ```bash gcloud auth login gcloud auth application-default login ``` 2. **Set Project**: Configure the active project for subsequent commands: ```bash 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. ```bash gcloud ai endpoints list \ --region=$LOCATION_ID ``` *(Optional)* To bound the result, use `--limit=$LIMIT`; `--page-size=$PAGE_SIZE` controls API chunking only and does NOT limit the total output. Use `--page-token=$PAGE_TOKEN` to continue from a previous batch. > [!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. ```bash gcloud ai endpoints describe $ENDPOINT_ID \ --region=$LOCATION_ID ``` The models deployed on an endpoint are its `deployedModels` in this output. In the Python SDK they are `endpoint.gca_resource.deployed_models`; `aiplatform.Endpoint` has no `deployed_models` attribute. ## 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.** ```bash gcloud ai endpoints create \ --region=$LOCATION_ID \ --display-name="my-endpoint" ``` The command has no `--asynchronous` flag: it waits for the operation and prints the new endpoint's resource name, whose last segment is the endpoint ID. > [!IMPORTANT] > > **You MUST seek interactive confirmation first.** Your confirmation prompt > **MUST** show the literal command string. For example: > > ```bash > gcloud ai endpoints create --region=$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.** ```bash 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: > > ```bash > 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.** ```bash 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.** ```bash # 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.
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