From Code to Cloud: 3 Labs for Deploying Your AI Agent

You've built a powerful AI agent. It works on your local machine, it's intelligent, and it's ready to...

You've built a powerful AI agent. It works on your local machine, it's intelligent, and it's ready to meet the world. Now, how do you take this agent from a script on your laptop to a secure, scalable, and reliable application in production? On Google Cloud, there are multiple paths to deployment, each offering a different developer experience.

If you are looking for a detailed architectural comparison to help you choose between Cloud Run, Google Kubernetes Engine (GKE), and Vertex AI Agent Engine, you can start by reading Choosing the Right Deployment Path for Your Google ADK Agents.

Ready to build? As part of our Production-Ready AI on Google Cloud Learning Path, we've created three distinct hands-on labs to help you experience these deployment options for yourself.

The Managed Solution: Vertex AI Agent Engine

For teams seeking the simplest path to production, Vertex AI Agent Engine removes the need to manage web servers or containers entirely. It provides an opinionated environment optimized for python agents, where you define the agent's logic, and the platform handles the execution, memory, and tool invocation.

{% card %} Start the lab! Lab: Create multi agent system with ADK, deploy in Agent Engine and get started with A2A protocol

Objective: Deploy a multi-agent system without provisioning any infrastructure, leveraging built-in capabilities for state management and reasoning while Google manages the runtime. {% endcard %}

The Serverless Experience: Cloud Run

For teams that want the flexibility of containers without the operational overhead, Cloud Run abstracts away the infrastructure, allowing you to deploy your agent as a container that automatically scales up when busy and down to zero when quiet.

This path is particularly powerful if you need to build in languages other than Python, use custom frameworks, or integrate your agent into existing declarative CI/CD pipelines.

{% card %} Start the lab! Lab: Build and deploy an ADK agent on Cloud Run

Objective: Containerize a tool-using agent with the Google Cloud ADK and deploy it to a secure public HTTPS endpoint to experience the speed of a serverless workflow. {% endcard %}

The Orchestrated Experience: Google Kubernetes Engine (GKE)

For teams that need precise configuration over their environment, GKE is designed to manage that complexity. This path shows you how an AI agent functions not just as a script, but as a microservice within a broader orchestrated cluster.

{% card %} Start the lab! Lab: Deploy ADK agents to Google Kubernetes Engine (GKE)

Objective: Deploy an ADK agent into a managed Kubernetes cluster, configuring autoscaling and precise resource limits using industry-standard tooling and manifests. {% endcard %}

Your Path to Production

Whether you are looking for serverless speed, orchestrated control, or a fully managed runtime, these labs provide the blueprint to get you there.

These labs are part of the Deploying Agents module in our official Production-Ready AI with Google Cloud program. Explore the full curriculum for more content that will help you bridge the gap from a promising prototype to a production-grade AI application.

Share your progress and connect with others on the journey using the hashtag #ProductionReadyAI. Happy learning!