Multi-Agent

Most agent tutorials show you the happy path. They give you a chat UI, a tool, and a one-shot demo. Then you try to build something real, and the demo collapses under a single missing concept: how state actually flows, how to test a non-deterministic system, what to do when a tool takes ten minutes instead of ten milliseconds, or how two agents from different teams find each other in production. The 5-Day AI Agents course on Kaggle is the closest thing I have found to a structured curriculum that covers the full life-cycle of an agent system, not just the demo. Ten codelabs, five whitepapers, and a single coherent framework that ties it all together. I worked through every codelab end-to-end. This post is the tutorial I wish I had read first: what each lab is actually trying to teach, the actual code from the codelabs, how to think through the exercises, and the parts of the material that translate cleanly into a real production system. The patterns in the course are not tied to one framework. The same ideas show up in LangGraph, CrewAI, AutoGen, and any custom agent runtime. I am using the course’s framework vocabulary here, but the lessons are not. ...

I have a bad habit. When I read a good blog post, I read it once, nod, and never actually try the thing it describes. This time I was determined to break that pattern. The post was Cloudflare’s “Build Your Own Vulnerability Harness”. It described a 6-phase multi-agent security audit pipeline. I had been wanting to test something like this for months. So I read the post, closed the tab, and opened a terminal. This is what happened next. The good parts, the embarrassing parts, and the part where I found a real authentication bypass in a 6,000-line Python library that downloads 50 million times a month. ...