/agent-platform-deploy

name: agent-platform-deploy
metadata:
  category: AiAndMachineLearning
description: >-
  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).

Agent Platform Model Garden Deploy Skill

This skill provides instructions for deploying Open Models from Agent Platform Model Garden to endpoints, and subsequently undeploying them to clean up resources.

1P Tuned Model Copy & Deployment

If you need to copy a **1P (First-Party) Tuned Model** from a source project to a destination region or project and deploy it to a newly created endpoint, refer to the [1P Tuned Model Copy & Deployment Guide](references/copy_deploy_guide.md).

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`, `list-deployment-config`)**

• **Rule**: No confirmation needed. You may execute these commands

immediately to gather information for the user. 2. **Tier M: Mutating & Reversible (`deploy`, `undeploy-model`)**

• **Rule**: This requires explicit user confirmation. You MUST present a

clear confirmation prompt to the user explaining the proposed command. You MUST wait for their explicit confirmation before executing. For `undeploy-model`, you MUST first verify that the endpoint and deployed model exist; if `describe` or `list` returns a 404 or empty result, you MUST halt and inform the user rather than attempting undeployment.

• **Same-turn restriction**: Do not run the command in the same turn as

presenting the confirmation prompt. End your turn after asking and wait for the user's reply; only execute after explicit approval. Printing a preview and then calling the tool before the user can answer does not count as obtaining confirmation. 3. **Tier D: Destructive & Irreversible (`delete`)**

• **Rule**: This requires **explicit typed confirmation**. You MUST output

a text message explaining the irreversible nature of endpoint or model deletion and asking the user to type "I confirm" or "Yes, delete it" before executing the deletion command.

1. Prerequisites

Before deploying, ensure you have the correct project and region set. The commands below use placeholder variables `PROJECT_ID` and `LOCATION_ID`.

Ensure you are authenticated:

gcloud auth login
gcloud auth application-default login
gcloud config set project $PROJECT_ID

2. Discovering Deployable Models

You can list models available in Model Garden and check if they can be self-deployed.

gcloud ai model-garden models list

To see what machine types and accelerators are supported for a specific model, pass a `MODEL_ID` you obtained from the `models list` output above. Substitute `<PUBLISHER>/<FAMILY>@<VERSION-ID>` below with the exact string from the catalog output — the placeholder is deliberately not a real model ID:

gcloud ai model-garden models list-deployment-config \
    --model="<PUBLISHER>/<FAMILY>@<VERSION-ID>"

> [!NOTE] Some models, especially Hugging Face models, might require a Hugging > Face Access Token for deployment.

> [!TIP] **Model Recommendation Instructions:** Whenever you are about to name a > specific model version in a response, do NOT recommend from memory. This > applies in all of the following situations — not just direct deploy requests: > > * The user asks to deploy a model without naming one. > * You are volunteering a next-step suggestion after a `list`, `describe`, or > `undeploy` operation (e.g. "Would you like me to deploy `<model>` to this > endpoint?"). > * The user asks a general "what should I use?" / "what's a good model for > X?" question. > * You are filling in a `MODEL_ID` value in an example command you are > showing the user (as opposed to a placeholder like > `<PUBLISHER>/<FAMILY>@<VERSION-ID>`). > > New model versions ship frequently and older ones may be deprecated, so > training-corpus knowledge of which models exist is unreliable. Follow this > procedure: > > 1. **Clarify the use case** if it isn't already clear from context (task > type, quality vs. latency vs. cost priorities, hardware/quota constraints, > license constraints). Skip if the user has already given enough signal. > 2. **Query the live catalog** with `gcloud ai model-garden models list`. > Narrow with `--filter` when appropriate (e.g. `--filter="name~gemma"`, > `--filter="name~llama"`, `--filter="name~qwen"`, > `--filter="name~deepseek"`). Never name a specific model version to the > user until you have seen it in the catalog output for this project. > 3. **Pick the latest generally-available version** in the family that fits > the use case. When multiple size variants exist, pick the one that matches > the user's hardware/cost tolerance. Prefer a newer major version over an > older one unless it is marked preview/experimental and the user explicitly > asked for a stable op