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Build AI Agents Using Google ADK

Agent Deployment

Explore the complete process of deploying AI agents built with Google ADK to Google Cloud Run. Learn how to prepare your project, create a cloud environment, manage dependencies, and use the ADK deployment tools to package and launch your agent as a scalable, serverless application accessible from anywhere.

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Building and testing an AI agent on a local machine is a critical phase of development. It allows for rapid iteration, debugging, and validation of the agent’s core logic. However, for an agent to become a useful application, it can’t remain confined to a single developer’s environment. It must be deployed.

Deployment is the process of taking a completed software application and making it available for use in a production environment. This involves packaging the application’s code and dependencies, placing them on a reliable and scalable server, and exposing them so that users or other services can access them. While ADK agents can be deployed to a wide range of environments, including the Vertex AI Agent Engine or any custom infrastructure that supports Docker, our focus will be on a powerful and developer-friendly platform: Google Cloud Run.

Understanding Google Cloud Run

Google Cloud Run is a fully managed, serverless platform designed to run containerized applications. It abstracts away all the complexity of server management, allowing us to focus solely on our application’s code.

Cloud Run is an ideal environment for deploying agentic services for several key reasons:

  • Serverless: We do not need to provision, configure, or manage any underlying virtual machines or servers. Google handles all the infrastructure, patching, and maintenance, which significantly reduces operational overhead.

  • Scales to zero: This is a powerful feature for applications with intermittent traffic, like many agent services. When our agent is not receiving any requests, Cloud Run can automatically scale the number of running instances down to zero. This means we pay nothing for idle time.

  • Automatic scaling: When traffic to our agent increases, Cloud Run automatically and rapidly starts new instances of our container to handle the load. When traffic subsides, it scales them back down. This ensures our application is always responsive without us having to manually adjust capacity.

  • Container-based: Cloud Run is built on the standard of containers (specifically Docker containers). This means any application that can be packaged into a container can run on Cloud Run, providing immense flexibility and portability.

Hands-on deployment to Cloud Run

We will now walk ...