I've been running a 5-agent AI system in production for several months. Not a demo. Not a weekend project. A system that actually does work autonomously while I sleep. Most articles about multi-agent AI are either toy examples or vendor marketing. This one is neither. Here's what actually works, what breaks, and how I structured the whole thing. Why Single-Agent AI Fails at Scale A single agent handling a complex task is like asking one person to simultaneously research a problem, write the code, review it for bugs, and deploy it — while keeping everything in their head. Context windows are the first wall you hit. When you stuff a 15-step task into a single prompt, the model starts losing coherence around step 8. It hallucinates, contradicts itself, or forgets constraints you stated at the top. The second problem is error propagation. A single agent that makes a bad assumption early carries that error through every subsequent step. There's no checkpoint. The third problem is specialization.…