Agent Platform Model Registry

Agent Platform Model Registry Management

Overview

This skill provides instructions for managing machine learning models in the Agent Platform Model Registry. It covers listing models, describing model details, uploading new models or versions, updating metadata, and deleting models.

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.
2. Tier M: Mutating & Reversible (upload, 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, --project=..., --display-name="...") — natural-language paraphrases are NOT sufficient.
   • Same-turn restriction: NEVER execute the command in the same turn as receiving the request or presenting the confirmation prompt! In Turn 1, you MUST ONLY present the interactive confirmation card with the exact, literal command string. Stop and wait for the user's reply; only execute in the subsequent turn after explicit 'Yes' / approval. Executing upload or update in Turn 1 without prior confirmation is strictly prohibited.
   • Mid-flow parameter changes / rejection: If the user rejects the prompt or changes any parameters (e.g., display name, description, parent model), do NOT execute the old command. Adapt immediately and present a NEW confirmation prompt with the updated literal command and wait for approval.
3. 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 check if the model is deployed to endpoints 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.
   • Mid-flow target changes: If the user changes their mind (e.g., "delete the second model instead"), do NOT delete the first model. Present a fresh typed confirmation prompt for the newly selected model ID and wait for approval.
4. Cost Estimation: Model Registry operations manage catalog metadata and stored model artifacts without provisioning serving compute or endpoints. Do NOT call the estimate_cost tool for Model Registry actions, as estimate_cost is designed for serving infrastructure (endpoints/batch prediction) and will return an error if called for registry operations. If including cost in the preview card, state that Model Registry operations incur no serving compute charges ($0.00 compute charges; standard Cloud Storage pricing applies to model artifacts).

Phase 0: Environment Setup & Parameter Resolution

CRITICAL: Before running any commands, verify that all necessary parameters are known:

1. Missing Region or Project: Follow the base environment grounding policy: if a session location or project is already set from prior turns, reuse it without re-asking. If missing from both prompt and session context, at most one direct lookup is permitted (e.g. gcloud config get project or gcloud config get compute/region). If still unresolved or ambiguous, pause and explicitly ask the user for the missing parameter before executing mutating or resource-specific commands.
2. Missing Model ID: If the user asks to update or describe a model without providing the model ID, pause and ask the user for the model ID, or offer to list models first to help them find it.
3. Placeholder Substitution: If the user's requested display name contains a placeholder token (e.g., <unique-suffix>, [suffix], or <timestamp>), generate a short unique alphanumeric string or timestamp and substitute it cleanly. Never pass unexpanded literal placeholder tokens to the API.
4. Region and Project Flags: Always pass --region=$LOCATION_ID and --project=$PROJECT_ID explicitly on all gcloud ai models commands. Do NOT use global.