Step-by-Step Guide to Deploy ADK Agents to Agent Engine

Prerequisites

• Google Cloud account
• Python 3.9
• Basic understanding of Python programming

And provide the Vertex AI User role Service Account.

1. Navigate to IAM in the console.
2. Click the checkbox to Include Google-provided role grants.
3. Find the AI Platform Reasoning Engine Service Agent (service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com) and click the pencil icon in its row.
4. Click Add another role.
5. Select Vertex AI User.

Create Project Structure

├Dir -->adk_to_agent_engine
── requirements.txt
└── transcript_summarization_agent
    ├── agent_engine_utils.py
    ├── agent.py
    ├── deploy_to_agent_engine.py
    ├── __init__.py
    ├── __pycache__
    │   └── agent.cpython-312.pyc
    ├── query_app_on_agent_engine.py
    └── test_agent_app_locally.py

2. .env File

GOOGLE_GENAI_USE_VERTEXAI=1
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1
MODEL=gemini-2.0-flash-exp
APP_NAME="Transcript Summarizer"

3. Requirements.txt

fire==0.7.0
google-cloud-aiplatform[adk,agent_engines]==1.88.0

4. Agent.py

import os
from dotenv import load_dotenv

from google.adk import Agent

load_dotenv()

root_agent = Agent(
    name="transcript_summarization_agent",
    description="Summarizes chat transcripts.",
    model=os.getenv("MODEL", "gemini-2.0-flash-exp"),
    instruction="Summarize the provided chat transcript.",
)

5. Test_agent_app_locally.py

import logging
import google.cloud.logging
import asyncio

from vertexai.preview import reasoning_engines

from agent import root_agent

logging.basicConfig(level=logging.INFO)
cloud_logging_client = google.cloud.logging.Client()
cloud_logging_client.setup_logging()

async def main():

    agent_app = reasoning_engines.AdkApp(
        agent=root_agent,
        enable_tracing=True,
    )

    session = await agent_app.create_session(user_id="u_123")

    for event in agent_app.stream_query(
        user_id="u_123",
        session_id=session.id,
        message="""
                Virtual Agent: Hi, I am a vehicle sales agent. How can I help you?
                User: I'd like to buy a car.
                Virtual Agent: Can I interest you in a boat?
                User: No, a car.
                Virtual Agent: This boat will be $10,000.
                User: Goodbye.
            """,
    ):
        logging.info("[local test] " + event["content"]["parts"][0]["text"])
        cloud_logging_client.flush_handlers()

if __name__ == "__main__":
    asyncio.run(main())

6. Deploy_to_agent_engine.py

import vertexai
from vertexai import agent_engines
from dotenv import load_dotenv

from agent import root_agent

load_dotenv()

vertexai.init(
    project=os.getenv("GOOGLE_CLOUD_PROJECT"),
    location=os.getenv("GOOGLE_CLOUD_LOCATION"),
    staging_bucket="gs://" + os.getenv("GOOGLE_CLOUD_PROJECT")+"-bucket",
)

remote_app = agent_engines.create(
    display_name=os.getenv("APP_NAME", "Agent App"),
    agent_engine=root_agent,
    requirements=[
        "google-cloud-aiplatform[adk,agent_engines]"
    ]
)

7. Query_app_on_agent_engine.py

import os
from dotenv import load_dotenv
import logging
import google.cloud.logging
from google.cloud.logging.handlers import CloudLoggingHandler

import vertexai
from vertexai import agent_engines

# Load environment variables and initialize Vertex AI
load_dotenv()
project_id = os.environ["GOOGLE_CLOUD_PROJECT"]
location = os.environ["GOOGLE_CLOUD_LOCATION"]
app_name = os.environ.get("APP_NAME", "Transcript Summarizer")
bucket_name = f"gs://{project_id}-bucket"

# Initialize Google Cloud Logging with the correct project ID
cloud_logging_client = google.cloud.logging.Client(project=project_id)
handler = CloudLoggingHandler(cloud_logging_client, name="transcript-summarizer")
logging.getLogger().setLevel(logging.INFO)
logging.getLogger().addHandler(handler)

# Initialize Vertex AI with the correct project and location
vertexai.init(
    project=project_id,
    location=location,
    staging_bucket=bucket_name,
)

# Filter agent engines by the app name in .env
ae_apps = agent_engines.list(filter=f'display_name="{app_name}"')
remote_app = next(ae_apps)

logging.info(f"Using remote app: {remote_app.display_name}")

# Get a session for the remote app
remote_session = remote_app.create_session(user_id="u_456")

transcript_to_summarize = """
    Virtual Agent: Hi, I am a vehicle sales agent. How can I help you?
    User: I'd like to buy a boat.
    Virtual Agent: A big boat, or a small boat?
    User: How much boat will $50,000 get me?
    Virtual Agent: That will get you a very nice boat.
    User: Let's do it!
"""

# Run the agent with this hard-coded input
events = remote_app.stream_query(
    user_id="u_456",
    session_id=remote_session["id"],
    message=transcript_to_summarize,
)

# Print responses
for event in events:
    for part in event["content"]["parts"]:
        if "text" in part:
            response_text = part["text"]
            print("[remote response]", response_text)
            logging.info("[remote response] " + response_text)

cloud_logging_client.flush_handlers()

Execution Steps:

Install ADK and Set Up Environment

# Add ADK to your PATH
export PATH=$PATH:"/home/${USER}/.local/bin"

# Install the Google ADK package
python3 -m pip install google-adk==1.1.1

# Install required dependencies
python3 -m pip install -r requirements.txt

Test locally

python3 transcript_summarization_agent/test_agent_app_locally.py

Deploy to Agent Engine

python3 transcript_summarization_agent/deploy_to_agent_engine.py

Query the deployed agent

python3 transcript_summarization_agent/query_app_on_agent_engine.py

List and delete agents deployed to Agent Engine

8. Agent_engine_utils.py

import os
import fire
from dotenv import load_dotenv
import vertexai
from vertexai import agent_engines

# Load environment variables and initialize Vertex AI
load_dotenv()
vertexai.init(
    project=os.getenv("GOOGLE_CLOUD_PROJECT"),
    location=os.getenv("GOOGLE_CLOUD_LOCATION"),
    staging_bucket="gs://" + os.getenv("GOOGLE_CLOUD_PROJECT") + "-bucket")

# Utility functions for working with Agent Engine
def list():
    """List Agent Engine agents."""
    for agent in agent_engines.list():
        print(agent.display_name)
        print(agent.resource_name + "\n")

def delete(resource_name):
    """Delete an Agent Engine agent by its resource_name."""
    agent_engines.delete(resource_name, force=True)

if __name__ == "__main__":
    fire.Fire()

python3 agent_engine_utils.py list

python3 agent_engine_utils.py delete RESOURCE_ID_FROM_PREVIOUS_OUTPUT

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