Welcome to this edition of Ctrl+Alt+Deploy 🚀
I’m Lauro Müller and super happy to have you around 🙂 Let’s dive in right away!
Let me share a secret: you don’t need those fancy multi-agent AI frameworks to create your AI agents. Instead, you can do it in 5 minutes and with a single YAML file. How? Let me show it to you.
Why Build an AI Agent?
We've all experienced the magic of modern AI. Tools like GPT-5 and Claude are incredible generalists, capable of writing code, drafting emails, and answering complex questions. They're the ultimate solo assistants. But in development, the most important work is rarely a solo job. It requires a team of specialists: a product manager to define the scope, a user advocate to clarify the "why", an architect to research the "how", software engineers to implement the “what”, and so on and so forth.
A simple AI prompt can feel smart, but it's often brittle. A truly agentic system is different. It's a flexible, multi-step workflow that can handle an entire class of problems. Today, we'll build exactly that: a robust team of AI agents that can turn any vague idea into a well-defined feature, using Docker's open-source tool, cagent.
Our Goal: Build an Automated Code Generation and Review Pipeline
We've all had AI write a function for us. We give it a prompt, and it generates a snippet of code. It feels like magic, but it’s often just the first step. The code might work, but is it production-ready? Does it have tests? Has it been reviewed for security and style? Does it have clear documentation?
A single AI, no matter how powerful, is a brilliant generalist. But professional software development is a multi-stage process handled by a team of specialists. What if you could build an AI team that mimics that very process?
We’ll move beyond a simple "write me a function" task and build something far more robust. Our goal is to create an agentic team that takes a high-level feature request and takes it through a complete mini-development lifecycle.