Harness Engineering: Building the System Around Your AI Coding Agent

AI coding agents feel like magic. It is easy to assume there is something exotic inside, some secret sauce that makes agents reliable. Well, there isn't, the core of every AI coding agent is very simple: send a message to a model, parse tool calls, execute them, feed the results back, repeat. Everything else (context management, permissions, observability, safety guardrails) is engineering layered on top of it that we should be building.

In this deep dive, we build that engine from scratch (in Java!), live on stage. Not to build the best agent, but to understand how the pieces fit together. We point it at a real codebase, and watch what happens. It compiles. Tests pass. And it violates every convention the team agreed on. So we iterate. We add context, constraints, and feedback, and at each step we examine what changed, why it helped, and what it maps to in the tools you already use.

The goal is a mental model. By the end, you will understand the components inside the AI coding tools you use every day, what you can layer on top to get smoother results and safer expectations, and where the honest limits still are, the gap no amount of engineering has closed yet.