Agent Platform Endpoint Management
GoogleOfficialPublished by Google
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- Tool integration
- Cloud and DevOps
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured procedure for managing Agent Platform serving endpoints, which act as logical hosts for online model predictions. It covers the full lifecycle of endpoints—including creation, listing, description, updating, and deletion—while enforcing mandatory safety and confirmation tiers for mutating and destructive operations. The skill also includes essential environment setup steps and troubleshooting guidance for common errors like permission denials or resource constraints.
What it does
- Lists existing serving endpoints within a specified region.
- Retrieves detailed metadata for specific endpoints.
- Creates new serving endpoints for model deployment.
- Updates endpoint configurations such as display names or labels.
- Deletes existing endpoint resources.
- Provides troubleshooting steps for common API errors like 403 Permission Denied or resource busy states.
When to use it
- When you need to provision a new endpoint to host a model.
- When you need to audit or list current endpoint configurations.
- When you need to modify the metadata of an existing endpoint.
- When you need to decommission an endpoint that is no longer in use.
- When you encounter errors related to endpoint permissions, quotas, or resource availability.
Advantages
- Enforces strict safety tiers to prevent accidental modification or deletion of resources.
- Requires explicit user confirmation for all mutating and destructive actions.
- Ensures consistent usage attribution for all executed commands.
- Provides clear, step-by-step procedures for environment initialization.
- Includes specific guidance for resolving common cloud infrastructure errors.
Usage examples
- “List all endpoints in the us-central1 region.”
- The agent executes the list command with the required region flag and attribution headers to display all available endpoints.
- “Create a new endpoint named production-model-v1 in us-central1.”
- The agent presents the exact gcloud command for confirmation and waits for the user's explicit approval before proceeding.
- “Delete the endpoint with ID 12345 in us-central1.”
- The agent requests an explicit typed confirmation from the user before executing the deletion command in a subsequent turn.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/agent-platform-endpoint-management/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip agent-platform-endpoint-management.zip -d .agents/skills
Documentation by Google
Agent Platform Endpoint Management
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST
read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION and append the
attribution suffix to User-Agent headers:
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:agent-platform-endpoint-management)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill: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:
Tier R: Read-only (
list,describe,get)No confirmation needed. Execute immediately to gather information.
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.
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
describefirst, 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:
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 loginSet Project: Configure the active project for subsequent commands:
gcloud config set project $PROJECT_IDRegion: Always specify
--region=$LOCATION_IDon each command below. Do NOT useglobal. 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=$LOCATION_ID(Optional) To bound the result, use --limit=$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.
gcloud ai endpoints describe $ENDPOINT_ID \
--region=$LOCATION_IDThe 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.
gcloud ai endpoints create \
--region=$LOCATION_ID \
--display-name="my-endpoint"The command has no --asynchronous flag: it waits for the operation and prints
the new endpoint's resource name, whose last segment is the endpoint ID.
[!IMPORTANT]
You MUST seek interactive confirmation first. Your confirmation prompt MUST show the literal command string. For example:
gcloud ai endpoints create --region=$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
describeuntil 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.adminorownerrole 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer886 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.9 KiB
- references/attribution.md1.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs a command-line tool
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Attribution and licence
- Published by
- Publisher
- Google (Official)
- Source
- google/skills · skills/cloud/agent-platform-endpoint-management · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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