Everyone talks about the model. Nobody talks about the harness. Give Claude Sonnet or GPT-4o a chat interface and you get a conversational AI. Wrap it in a loop that can call external tools, maintain state across turns, enforce budget limits, and validate its own outputs — and you get an agent. The difference isn't the LLM. It's everything around the LLM. The AWS team published a guide on "agent harnesses" this week, and it got me thinking: most tutorials show you how to call an LLM or how to register a tool. Almost none show you the orchestration layer that makes those individual pieces behave as a coherent system. I've built agents that run autonomously on production infrastructure 24/7. The mistakes I made early on weren't about picking the wrong model. They were about skipping the harness — assuming the model would "just figure it out." It won't. The harness is what makes an agent reliable, and reliability is the only metric that matters once you move past the demo phase.…