Multi-Agent AI Systems: Architecture Patterns That Actually Work

Single agents break in boring ways. You hit context limits, tools start interfering with each other, and the more capable you try to make one agent the worse it performs on any individual task. The solution most people reach for — just make the prompt bigger — is the wrong answer. Multi-agent systems are the right answer, but they introduce a different class of problem: coordination, trust, and failure modes that are harder to debug than a bad prompt. This post is about the architecture patterns I've landed on after running multi-agent systems in a homelab environment where the stakes are real (it controls actual infrastructure) but forgiving enough to experiment. Why Split Into Multiple Agents At All? Before getting into patterns, it's worth being honest about the tradeoffs. Multi-agent systems are more complex. They have more failure points. Debugging a chain of three agents is significantly harder than debugging one. You only pay that cost if you're getting something back. The thing