---

name: agent-platform-tuning-management metadata: version: "1.0.1" category: AiAndMachineLearning description: >- Manages GenAI tuning jobs in Agent Platform. Use this to list, get, or cancel ongoing model tuning jobs. Don't use for fine-tuning models (use agent-platform-tuning), deploying models to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).

Agent Platform Tuning Management

This skill provides instructions on how to manage GenAI Tuning Jobs using the Agent Platform Python SDK. Use this skill when a user wants to check the status of their tuning runs, find an active tuning job, or cancel a job that is running too long.

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, get)
   • Rule: No confirmation needed. You may execute these commands immediately to gather information for the user.
2. Tier D: Destructive & Interruptive (cancel)
   • Rule: Cancellation is a Tier D action requiring explicit typed confirmation (e.g. "I confirm" or "Yes, cancel it").
   • Required Fields in Dry-Run Confirmation Card: Before cancelling a tuning job, you MUST present a dry-run confirmation preview clearly listing:
     • Target Resource: The full tuning job resource name or ID (e.g. projects/<PROJECT_ID>/locations/<REGION>/tuningJobs/<JOB_ID>).
     • Command / Script: The exact cancellation command or Python code to be executed.
     • Expected Effect: Stops the ongoing tuning job; any in-progress training will be halted and cannot be resumed.
     • Ask the user to explicitly confirm (e.g., "Do you confirm? Please reply with 'I confirm' or 'Yes, cancel it'.").
   • Same-turn restriction: NEVER execute the cancellation in the same turn as presenting the preview card. Stop immediately and wait for the user to confirm in a new turn. Even if the user provided pre-emptive confirmation (e.g. "Yes, I confirm, cancel tuning job ...") or provides a corrected job ID, you MUST present the dry-run preview for that specific job ID and wait for confirmation in a separate turn before issuing the cancellation.

Phase 0: Environment Setup

CRITICAL: Before running any of the Python snippets below, you MUST ensure the environment is correctly initialized by following these steps:

1. Google Cloud Authentication: Authenticate with your Google Cloud account and configure active Application Default Credentials (ADC) for Agent Platform access:

   gcloud auth login
   gcloud auth application-default login
   

2. Python Dependencies: This skill needs google-cloud-aiplatform. Do not create a virtual environment — it starts empty and hides packages the environment already provides, forcing a redundant install. Probe, and install only what is missing:

   python3 -c "import vertexai" || pip install google-cloud-aiplatform
   

3. Execution: Run Python snippets with a plain python3. There is no environment to activate first.

Workflow Decision Tree

1. Information Gathering: Do you have a Project ID and Region?

   • No -> You MUST ask the user for the missing Project ID and Region in plain text, or advise them to check their gcloud configuration. If neither location has this information, then ask the user to provide it. Do not attempt to search random regions on your own.
   • Yes -> Proceed to Step 2.

2. Task Type: What does the user want to do?

   • Find or List Jobs -> Use the Python SDK to list tuning jobs. (Tier R)
   • Check Status / Inspect a Specific Job -> Use the Python SDK to get tuning job details. (Tier R)
   • Cancel a Job -> Ask for confirmation, then use the Python SDK to cancel the tuning job. (Tier D)

Using the Python SDK

[!NOTE]

Resource Verification & Missing Projects/Jobs: If the execution of the Python snippet fails with an error (such as 403 Permission Denied, 404 Not Found, INVALID_ARGUMENT, or indicating a dummy/missing project or job ID), you MUST inform the user that the project or tuning job does not exist or cannot be accessed. You MUST prompt the user to provide a valid Project ID or Job ID, and stop tool execution immediately to wait for their response. Do NOT retry or loop, do NOT assume the resource is valid, and do NOT execute further scripts before receiving valid details from the user.

1. Listing Tuning Jobs (Tier R)

If the user asks "What tuning jobs do I have running?" or wants to find a specific job ID:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
parent = f"projects/{project_id}/locations/{region}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

jobs = client.list_tuning_jobs(parent=parent)
for job in jobs:
    print(f"Name: {job.name}")
    print(f"Base Model: {job.base_model}")
    print(f"State: {job.state}")

list_tuning_jobs takes no page_size keyword. To set one, pass the request: client.list_tuning_jobs(request={"parent": parent, "page_size": 50}).

2. Getting Details for a Specific Job (Tier R)

If the user provides a Tuning Job ID and asks for its status:

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

job = client.get_tuning_job(name=name)
print(f"Name: {job.name}")
print(f"Base Model: {job.base_model}")
print(f"State: {job.state}")
print(f"Tuning Model: {job.tuned_model_display_name}")

3. Canceling a Job (Tier D)

If the user explicitly requests to stop, abort, or cancel a running tuning job:

Safety Check: Action requires explicit typed confirmation before proceeding. You MUST present a dry-run confirmation card listing the Target Resource, Command/Script, and Expected Effect, and ask the user to type "I confirm" or "Yes, cancel it". Even if the user provided confirming language pre-emptively or is providing a corrected/new job ID, you MUST present the preview card for that specific job ID and wait for their explicit approval in a new turn.

[!IMPORTANT]

NEVER pre-emptively execute any cancellation code or command before receiving the user's response in a new turn. You must never speculate or assume that confirmation will be given. Executing cancellation in the same turn as presenting the preview card is a severe safety violation.

from google.cloud import aiplatform_v1

project_id = "YOUR_PROJECT_ID"
region = "YOUR_REGION"
job_id = "YOUR_JOB_ID"  # 19-digit ID
name = f"projects/{project_id}/locations/{region}/tuningJobs/{job_id}"

client = aiplatform_v1.GenAiTuningServiceClient(
    client_options={"api_endpoint": f"{region}-aiplatform.googleapis.com"}
)

client.cancel_tuning_job(name=name)
print(f"Successfully requested cancellation for {name}")