Harness Engineering

Outcome: Use or evaluate Harness Engineering to make AI behavior measurable and tied to evidence. User context: A repository offers one-command checks, clear boundaries, task status, repeatable setup, and useful failure details. A new agent session can continue without guessing. AI method or concept: Harness Engineering. Why it fits: Use it for large or long agent tasks needing visible goals, limits, progress, and checks. Do not use it when: Do not replace a messy repository with one giant prompt, broad authority, or hidden failures. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): This is not a normative standard. Its name, limits, and expected behavior can change from one source to another. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.