This hands-on lab is your gateway to deploying autonomous AI agents in production-ready environments. While many AI builders experiment locally, this session tackles the crucial next step: containerizing and cloud-deploying agents that can scale, communicate, and operate reliably in the real world. You'll transform a basic Echo-Agent into a cloud-native service running on Google Cloud Platform, establishing the foundation for sophisticated multi-agent architectures.
What You'll Build
Over three intensive hours, you'll create a fully containerized AI agent and deploy it to Google Cloud Run. This isn't theoretical — by the end of the session, you'll have a live agent accessible via a public URL, ready to serve as the building block for inter-agent communication systems. The Echo-Agent serves as a practical template: simple enough to deploy in one session, yet robust enough to demonstrate production deployment patterns.
The session covers Docker containerization fundamentals, FastAPI or Express.js endpoint design, and GCP Cloud Run deployment workflows. You'll learn how to package your agent logic, dependencies, and runtime into portable containers that abstract away infrastructure concerns. This foundation becomes critical in Sprint #5, where these containerized agents will communicate with each other to form autonomous systems.
The Lab Format
This is the fourth session in The Agentic AI Engineering Lab series, but each meetup functions as a standalone sprint. First-time attendees can jump straight in — while we build on previous results, every session provides a complete learning experience. Limited to just 6 participants, this micro-meetup prioritizes hands-on building over passive learning. Expect to spend most of your time coding, debugging, and deploying alongside fellow engineers.
The format balances brief technical context with extended build time. After quick introductions and a focused overview of Docker and GCP fundamentals, you'll dive into containerizing your agent. The session culminates in live demos where participants showcase their deployed agents, followed by a technical teardown discussing what worked, what broke, and what we learned.
Who Should Attend
This lab targets engineers ready to move AI agents from local experiments to production infrastructure. You should be comfortable with Python or JavaScript/TypeScript, have Docker installed and working on your laptop, and bring access to an LLM API (Gemini, [Claude](/tools/cl