Build more reliable AI coding workflows without chasing the perfect prompt.
AI coding agents can write code, inspect repositories, run tools, and complete multi-step tasks. But useful output is not the same as reliable engineering.
Harness Engineering is a practical guide to building the surrounding workflow that helps agents work more safely and predictably. Using a small companion project, agent_harness_lab, the book shows how vague tasks, weak context, loose permissions, and unclear verification can be improved step by step.
You will learn how to:
• Make repositories easier for agents to navigate
• Turn vague requests into executable task briefs
• Choose useful context without overwhelming the agent
• Set safer tool and permission boundaries
• Use plans, checkpoints, tests, and evidence more effectively
• Apply human review where it adds the most value
• Improve the harness over time by learning from repeated failures
This is not a prompt-writing guide or a vendor-specific manual. It is a practical framework for software engineers, technical leads, and builders who want AI agents to become part of a more trustworthy engineering workflow.
The tools will keep changing. The discipline behind a strong harness will last.
