Beyond AGENTS.md: Harness Engineering, Loop-Based Delivery, and Context-Aware Prompting

Most AI coding workflows still assume a simple pattern: give the model a prompt, let it write code, and then clean up whatever comes out. That can work for a small bug fix, a minor UI enhancement, or a narrow refactor. But once you are building real features in a complex production codebase, that model starts to break down. The problem is not just model quality. The problem is process. What matters is not whether an agent can produce code in one shot. What matters is whether the system around the agent can repeatedly turn messy user intent into production-safe changes. That means better orchestration, better artifacts, better feedback loops, and better context delivery. That is where harness engineering becomes useful. And in my view, the next step after a good harness is not a bigger AGENTS.md file. It is a context-aware prompting system that injects the right information at the right step of the loop. Harness Engineering & Agent-Guided Work Harness engineering starts with a simple sh