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Lab 35: Deploying an Agent to Agent Engine Challenge

Goal​

In this lab, you will deploy an ADK agent to Google Cloud's Agent Engine using both the recommended Accelerated method and the manual Standard method.

Prerequisites​

  • A Google Cloud Project with billing enabled.
  • gcloud CLI installed and authenticated (gcloud auth application-default login).
  • A GCS bucket for the Standard Deployment part (gsutil mb -p YOUR_PROJECT_ID -l us-central1 gs://YOUR_UNIQUE_BUCKET_NAME).
  • Python Environment: A Python version between 3.9 and 3.13.
  • UV Tool: For managing the Python environment. See Install UV.
  • Make Tool: A build automation tool, typically pre-installed on Unix-based systems.
  • Required APIs: Ensure the following APIs are enabled in your project:
    • Vertex AI API
    • Cloud Build API
    • Artifact Registry API
    • Cloud Resource Manager API
  • Set GCP Project: Before starting, ensure your gcloud CLI is configured to the correct project: 
    gcloud config set project YOUR_PROJECT_ID

This method uses the Agent Starter Pack (ASP) to add deployment artifacts to your existing ADK project and deploy it.

Step 1: Prepare the Agent Project​

  1. Get an Agent: For this lab, we'll use the multi_tool_agent from the Python Quickstart. If you don't have it, create it now. These instructions assume your project is in a directory structure like your-project-directory/multi_tool_agent/.
  2. Navigate to the Parent Directory: In your terminal, navigate to the parent directory that contains your agent folder (e.g., your-project-directory/).
  3. Enhance the Project: Run the ASP enhance command to add the required deployment files to your project. 
    uvx agent-starter-pack enhance --adk -d agent_engine
  4. Follow the Prompts: The tool will ask you several questions. You can accept the defaults, but ensure you select a supported region for Agent Engine (e.g., us-central1).

Step 2: Connect to Your Google Cloud Project​

  1. Login to Google Cloud: 
    gcloud auth application-default login
  2. Set Your Project ID: 
    gcloud config set project your-project-id-xxxxx
  3. Verify the Project: 
    gcloud config get-value project

Step 3: Deploy the Agent​

  1. Ensure you are in the parent directory (e.g., your-project-directory/).
  2. Run the Deployment Command: This command uses the Makefile added by the ASP tool to provision the cloud infrastructure and deploy your agent. This process can take several minutes. 
    make backend
  3. Find Your Agent: Once the build is complete, navigate to Vertex AI -> Agent Engine in the Cloud Console to find your deployed agent and its ID.

sidebar_position: 2

Part 2: Standard Deployment (Manual)​

This method involves writing a custom Python script to deploy the agent.

Step 1: Prepare the Agent Project​

  1. Get an Agent: Copy the multi_tool_agent project to a new directory named deploy-manual. 
    cp -r /path/to/multi_tool_agent deploy-manual
    cd deploy-manual
  2. Install Dependencies: 
    pip install "google-cloud-aiplatform[adk,agent_engines]>=1.111"

Step 2: Create the Deployment Script​

  1. In the deploy-manual directory, create a new file named deploy.py.

  2. Action: Write the Python code to deploy the agent. Use the skeleton below and fill in the # TODO sections. You will need to:

    • Import vertexai, agent_engines, and your root_agent.
    • Initialize the Vertex AI SDK with your project details.
    • Wrap your root_agent in an agent_engines.AdkApp.
    • Call agent_engines.create to deploy the app.
    # In deploy.py
    import vertexai
    from vertexai import agent_engines
    from multi_tool_agent.agent import root_agent # Make sure this import is correct

    # TODO: Fill in these values for your project
    PROJECT_ID = "your-gcp-project-id"
    LOCATION = "us-central1"
    STAGING_BUCKET = "gs://your-gcs-bucket-name"

    # Initialize the Vertex AI SDK
    vertexai.init(
        project=PROJECT_ID,
        location=LOCATION,
        staging_bucket=STAGING_BUCKET,
    )

    # Wrap the agent in an AdkApp object
    app = agent_engines.AdkApp(
        agent=root_agent,
        enable_tracing=True,
    )

    # TODO: Call agent_engines.create() to deploy the app.
    # Pass the `app` object to the `agent_engine` parameter.
    # Also provide a `requirements` list: ["google-cloud-aiplatform[adk,agent_engines]"]
    remote_app = None # Replace this

    print(f"Deployment finished!")
    print(f"Resource Name: {remote_app.resource_name}")

Action: Before deploying, add the local testing code from the lab-solution.md to your deploy.py script (before the deployment call) to test the AdkApp locally. This helps you catch errors before starting the lengthy deployment process.

Step 4: Deploy the Agent​

Run the deployment script. This will take several minutes.

python deploy.py

Step 5: Interact with the Deployed Agent​

  1. Create an interact.py script (code available in lab-solution.md).
  2. Action: Configure the script with your PROJECT_ID, LOCATION, and the AGENT_ENGINE_ID from the deployment output.
  3. Run the script to test your deployed agent: 
    python interact.py

Cleanup (Important!)​

Follow the cleanup instructions in lab-solution.md to delete the Agent Engine instances and GCS buckets to avoid incurring costs.

🕵️ Hidden Solution 🕵️​

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMzUtZGVwbG95bWVudC1hZ2VudC1lbmdpbmUvbGFiLXNvbHV0aW9u

The direct link is: Lab Solution