Harness Engineering
A development method that stabilizes AI coding quality with explicit, testable acceptance conditions
#Harness Engineering#verification harness#acceptance criteria#verification loop
What is Harness Engineering?
Harness Engineering is the practice of evaluating AI-generated outputs through explicit tests, acceptance criteria, and sample cases before release.
What changes in practice?
The focus shifts from "write better prompts" to "define machine-checkable done criteria." This keeps quality more stable even when models change.
When is it most useful?
It is especially effective for multi-file edits, long-running tasks, and repositories with high regression risk. The main value is reducing rework cost.
Related Terms
Related terms
development
Data Portability
The ability to export and import user data across services in reusable formats
development
Memory Import
A feature that transfers core user context from one AI system to another to accelerate personalization
development
Test-Driven Agentic Development (TDAD)
A method that defines pass/fail tests first before delegating implementation to AI agents