# How to Manage Vertex AI Agent Builder Deployments with Skills > Automate Vertex AI Agent Builder deployments using the agent-platform-deploy skill. Manage the full lifecycle from auth to safe deployment with gcloud CLI commands. - Repository: [Google/skills](https://github.com/google/skills) - Tags: how-to-guide - Published: 2026-09-02 --- **Use the `agent-platform-deploy` skill in the `google/skills` repository to automate the full lifecycle of Vertex AI Agent Platform deployments through a four-phase workflow that includes authentication, model discovery, cost-aware confirmation, and safe deployment with gcloud CLI commands.** Managing Vertex AI Agent Builder (now called **Vertex AI Agent Platform**) deployments requires careful coordination of authentication, resource selection, cost estimation, and cleanup procedures. The `google/skills` repository provides a dedicated `agent-platform-deploy` skill that encapsulates these operations into a structured, safety-gated workflow. This guide walks through the technical implementation found in the skill's source files, demonstrating how to deploy open-weights models from Model Garden while enforcing cost controls and safety tiers. ## Understanding the agent-platform-deploy Skill Architecture The skill is located at `skills/cloud/agent-platform-deploy/` within the `google/skills` repository and follows a strict four-phase process defined in [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md). Each phase maps to specific gcloud CLI operations and safety checks: 1. **Prerequisites & Authentication** – Validates gcloud credentials and project configuration 2. **Model Discovery** – Lists available Model Garden models and fetches deployment configurations 3. **Cost Estimation & Confirmation** – Computes hourly costs and enforces explicit user confirmation 4. **Deployment, Verification, and Cleanup** – Executes asynchronous deployment with endpoint testing and managed teardown The skill implements **Tier M** (mutating) and **Tier D** (destructive) safety gates to prevent accidental resource modifications or deletions without explicit confirmation. ## Prerequisites and Authentication Before executing any deployment commands, the skill requires active authentication and project configuration. According to [`skills/cloud/agent-platform-deploy/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/agent-platform-deploy/SKILL.md), you must run these commands to establish credentials: ```bash gcloud auth login gcloud auth application-default login gcloud config set project $PROJECT_ID ``` These commands ensure the gcloud SDK has both user credentials and application-default credentials required for Vertex AI API interactions. The skill validates this state before proceeding to model discovery to prevent mid-workflow authentication failures. ## Model Discovery and Selection The discovery phase identifies deployable models and their hardware requirements. The skill queries Model Garden to list available models and retrieve specific deployment configurations: ```bash # List all models in Model Garden gcloud ai model-garden models list # Get deployment config for a specific model gcloud ai model-garden models list-deployment-config \ --model="/@" ``` This step returns critical parameters including required machine types (e.g., `g2-standard-48`), accelerator specifications (e.g., `NVIDIA_L4`), and accelerator counts. The skill captures these values to populate the deployment script templates and cost estimation calculations. ## Cost Estimation and Safety Confirmation ### Calculating Hourly Costs The skill implements a three-tier cost estimation strategy as defined in [`skills/cloud/agent-platform-deploy/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/agent-platform-deploy/SKILL.md). It first attempts a live price lookup via the `estimate_cost` tool, falls back to the bundled Python script [`scripts/calculate_cost.py`](https://github.com/google/skills/blob/main/scripts/calculate_cost.py), and finally references the official pricing page if needed: ```bash python3 scripts/calculate_cost.py --machine-type=g2-standard-48 ``` The estimated hourly cost is presented with a disclaimer that it reflects list pricing and may differ from the actual bill. This transparency prevents unexpected charges before resource provisioning begins. ### Understanding Tier M Safety Gates Because deployment is a **Tier M** (mutating) operation, the skill enforces explicit user confirmation before executing any `gcloud ai model-garden models deploy` command. This safety mechanism, embedded in the SKILL file logic, requires affirmative user input to proceed with resource creation, protecting against accidental deployments. ## Deploying Models to Vertex AI Agent Platform ### Asynchronous Deployment Commands The skill generates a deployment script using placeholders discovered in previous phases. The deployment runs asynchronously to prevent terminal blocking during the provisioning process: ```bash PROJECT_ID=$(gcloud config get-value project) LOCATION_ID="us-central1" # adjust as needed MODEL_ID="/@" # replace with live ID gcloud ai model-garden models deploy \ --project=$PROJECT_ID \ --region=$LOCATION_ID \ --model=$MODEL_ID \ --machine-type="g2-standard-48" \ --accelerator-type="NVIDIA_L4" \ --accelerator-count=4 \ --endpoint-display-name="my-open-model-deployment" \ --asynchronous ``` The `--asynchronous` flag immediately returns an operation ID, allowing the workflow to continue with status monitoring rather than waiting for the full provisioning cycle. ### Monitoring Deployment Status To track the asynchronous operation, the skill provides commands to inspect the deployment progress: ```bash gcloud ai operations describe YOUR_OPERATION_ID --region=$LOCATION_ID ``` This command returns the current state (PENDING, RUNNING, SUCCESSFUL, or FAILED) and error details if the deployment encounters issues. The skill polls this status or provides it for manual monitoring depending on the execution mode. ## Verification and Testing ### Making Test Predictions via REST API Once the endpoint is active, the skill provides commands to extract the dedicated endpoint DNS and send test requests. This verification step confirms the deployment is serving traffic correctly: ```bash ENDPOINT_URL=$(gcloud ai endpoints describe $ENDPOINT_ID \ --project=$PROJECT_ID --region=$LOCATION_ID \ --format="value(dedicatedEndpointDns)") curl -X POST \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ "https://${ENDPOINT_URL}/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION_ID}/endpoints/${ENDPOINT_ID}/chat/completions" \ -d '{ "model": "'"$ENDPOINT_ID"'", "messages": [{"role":"user","content":"Explain quantum computing in simple terms."}] }' ``` This cURL command validates the full inference pipeline including authentication, network connectivity, and model responsiveness. ## Cleanup and Undeployment ### Tier M and Tier D Safety Operations The skill provides a complete cleanup workflow to stop billing when models are no longer needed. This involves **Tier M** operations (undeploying) followed by **Tier D** (destructive) operations (deletion): ```bash # List endpoints to find the correct ENDPOINT_ID gcloud ai endpoints list --project=$PROJECT_ID --region=$LOCATION_ID # List models to find the deployed model ID gcloud ai models list --project=$PROJECT_ID --region=$LOCATION_ID # Undeploy the model (Tier M - requires confirmation) gcloud ai endpoints undeploy-model $ENDPOINT_ID \ --project=$PROJECT_ID --region=$LOCATION_ID \ --deployed-model-id=$DEPLOYED_MODEL_ID # Delete the endpoint and model (Tier D - destructive, requires confirmation) gcloud ai endpoints delete $ENDPOINT_ID --project=$PROJECT_ID --region=$LOCATION_ID --quiet gcloud ai models delete $MODEL_ID --project=$PROJECT_ID --region=$LOCATION_ID --quiet ``` Each deletion command triggers the skill's safety tier logic, requiring explicit confirmation before removing resources and preventing accidental data loss. ## Summary - The `agent-platform-deploy` skill in `google/skills` provides a complete workflow for managing Vertex AI Agent Platform deployments through four distinct phases. - **Safety tiers** (Tier M for mutating, Tier D for destructive) enforce explicit confirmations before executing commands that modify or delete resources. - **Cost estimation** uses a fallback chain from live pricing to [`scripts/calculate_cost.py`](https://github.com/google/skills/blob/main/scripts/calculate_cost.py) to ensure users understand hourly expenses before deployment. - **Asynchronous deployment** with `--asynchronous` flags prevents workflow blocking while providing operation IDs for status monitoring. - The skill includes complete verification and cleanup scripts, ensuring users can test endpoints and stop billing when resources are no longer needed. ## Frequently Asked Questions ### What is the difference between Vertex AI Agent Builder and Vertex AI Agent Platform? Vertex AI Agent Builder was the original name for Google's managed service for deploying, serving, and monitoring large language-model agents. It has been rebranded as **Vertex AI Agent Platform**, though the underlying infrastructure and gcloud CLI commands remain consistent. The `agent-platform-deploy` skill in the `google/skills` repository references both terms but targets the current Platform implementation. ### How does the skill calculate deployment costs when live pricing is unavailable? The skill implements a three-tier fallback system defined in [`skills/cloud/agent-platform-deploy/SKILL.md`](https://github.com/google/skills/blob/main/skills/cloud/agent-platform-deploy/SKILL.md). It first attempts to query live pricing APIs via the `estimate_cost` tool. If that fails, it executes the local Python utility [`scripts/calculate_cost.py`](https://github.com/google/skills/blob/main/scripts/calculate_cost.py) with parameters like `--machine-type=g2-standard-48` to compute hourly costs based on predefined rates. If both automated methods fail, it directs users to the official Google Cloud pricing page for manual verification. ### Why does the deployment command require explicit confirmation? The deployment command is classified as **Tier M** (mutating) according to the skill's safety tier logic embedded in [`SKILL.md`](https://github.com/google/skills/blob/main/SKILL.md). Because deploying a model provisions billable resources including compute instances and accelerators, the skill enforces an explicit confirmation step before executing `gcloud ai model-garden models deploy`. This prevents accidental resource creation and unexpected cloud charges. ### Can I deploy a tuned model rather than an open-weights model from Model Garden? Yes, the skill supports deploying first-party (1P) tuned models through the workflow documented in [`skills/cloud/agent-platform-deploy/references/copy_deploy_guide.md`](https://github.com/google/skills/blob/main/skills/cloud/agent-platform-deploy/references/copy_deploy_guide.md). This guide describes how to copy a tuned model artifact to a new endpoint location before deployment, using the same authentication and safety mechanisms as open-weights deployments but with additional steps to handle the custom model registry.