From Code to Cloud: Three 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 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.