Why This Matters
Multi-agent systems sound amazing in blog posts: agents dividing tasks, reviewing each other's work, delivering super-human throughput. The reality on most teams is the opposite. Naive multi-agent makes tasks three times slower, five times more expensive, and produces worse output than a single agent would have. This guide catalogs the seven failure modes that cause that gap — agent ping-pong, context explosion, merge hell, infinite loops, cost spirals, latency cascades, and the god-supervisor — with concrete examples and the fix for each. By the end you will have a decision tree for when multi-agent actually pays off, the metrics to track if you deploy it, and the red flags that tell you to revert to single-agent before you burn through your API budget.
The Problem
You read about multi-agent systems. They sound amazing: agents working together, dividing tasks, reviewing each other's work. You implement it. Now your tasks take 3x longer, cost 5x more, and the output quality is worse than single-agent.
What happened? You fell into the multi-agent trap: more agents does not mean better results. Often it means expensive chaos.
Multi-agent has real use cases. But it also has anti-patterns - failure modes that are predictable, common, and expensive. Learn to recognize them before you burn through your API budget.
The Core Insight
Multi-agent architecture should solve a problem that single-agent can't. If single-agent works, don't add complexity.
Think of it like microservices vs monolith:
- Good microservices: Teams can deploy independently, scale different services differently, use best tool for each job.
- Bad microservices: 50 services for a simple app. Network overhead. Distributed debugging hell. Slower than a monolith.
Same with agents. Multi-agent is a tool, not a goal. Use it when it solves a problem. Avoid it when it adds overhead without benefit.