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Agentic AI Engineering Lab #4 – Cloud-Native Foundation: The Echo-Agent in a Box

Event artwork for Agentic AI Engineering Lab #4 – Cloud-Native Foundation: The Echo-Agent in a Box

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About this event

What to expect

This hands-on lab session takes you from local development to cloud-native deployment, teaching you how to containerize and deploy an AI agent to production infrastructure. Over three intensive hours, you'll transform a basic agent into a scalable, cloud-hosted service running on Google Cloud Run, creating the foundation for more advanced autonomous systems.

What You'll Build

The session focuses on practical implementation rather than theory. You'll work through the complete deployment pipeline: containerizing a Python/FastAPI or JavaScript/TypeScript agent using Docker, pushing your images to the cloud, and going live with a publicly accessible endpoint. By the end, you'll have a Dockerized agent with a stable URL — ready to scale and serve as the basis for inter-agent communication in future sprints.

Session Structure

This is a high-density micro-meetup limited to just six participants, ensuring everyone gets hands-on time and direct support. The format follows an "anthology series" approach where each session is a standalone sprint — you don't need to have attended previous labs to participate fully. After quick introductions at 10:00, you'll dive into a brief technical overview covering Docker fundamentals, containerization principles, and Google Cloud Platform basics. From 10:30 onwards, the focus shifts entirely to building: hands-on experimentation, troubleshooting, and cloud deployment. The session concludes with live demos where participants showcase their working deployments, followed by a technical teardown to discuss what worked, what broke, and what you learned.

Prerequisites and Setup

You'll need a Google Account for GCP access, a laptop with Docker installed and configured, and API access to an LLM provider (Claude, ChatGPT, Gemini, or similar). This lab is designed for engineers who learn by building — expect to write code, debug containers, and iterate rapidly. The "Build, Break & Iterate" philosophy means you'll be troubleshooting real deployment challenges rather than following a simple tutorial.

The Engineering Lab Philosophy

This series focuses on building truly autonomous systems that move beyond proof-of-concept scripts. Each sprint tackles a specific technical challenge in the journey toward production-ready agentic AI. This fourth session establishes the cloud-native foundation that will enable future work on inter-agent communication, scalabilit

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