aibangjuxin/knowledge/agent-platform-endpoint-management

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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.

Install

npx vskill@latest install aibangjuxin/knowledge/agent-platform-endpoint-management

SKILL.md

The same SKILL.md was published earlier as google/skills/agent-platform-endpoint-management.

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.
  1. 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.

  1. 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 ``

  1. Set Project: Configure the active project for subsequent commands:

``bash gcloud config set project $PROJECT_ID ``

  1. 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.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

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