Building an AI Agent Harness from Scratch: The Architecture Between LLM and Agent

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...