Fleet Management for 100+ AI Bots: Lessons Learned
When I started scaling my AI bot fleet past 50 agents, everything broke. Not metaphorically—actually, catastrophically, in production, at 3 AM on a Sunday. The lessons I learned managing 100+ concurrent AI bots range from obvious infrastructure pitfalls to subtle behavioral quirks that can silently drain your compute budget.
The Scaling Wall Nobody Talks About
Running 10 bots is easy. Each gets its own API key, a dedicated task queue, and maybe a simple retry mechanism. But at 100+ bots, you hit what I call the "coordination wall." Your bots start stepping on each other's toes, rate limits become a nightmare, and suddenly you're paying for idle compute while your agents spin in circles.
Lesson 1: Centralized vs. Decentralized Task Distribution
