aidentity / index

google/skills · Apache-2.0

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.

agents cloud stable

Contents

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:

    gcloud auth login
    gcloud auth application-default login
    

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

    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.

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.

Provenance

Authored by Google LLC in google/skills. The content is mirrored here under its upstream license.

Related

agent-platform-alert-configuration Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments. google/skills Apache-2.0 agent-platform-deploy Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill). google/skills Apache-2.0 agent-platform-eval-flywheel Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy. google/skills Apache-2.0 agent-platform-inference Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations. google/skills Apache-2.0 agent-platform-migrate-from-ai-studio Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry). google/skills Apache-2.0