Harness Engineering

Harness engineering is a practice of using deterministic tools, agent-based reviews, and periodic checks to ensure AI-generated code remains correct and consistent over time, preventing the gradual degradation that occurs when AI assistants drift from coding conventions and architectural decisions. The approach consists of three components: context engineering (maintaining a knowledge base documenting the project's stack, architectural decisions, and constraints for the AI), architectural constraints (enforcement points using either deterministic tools or AI-based reviews), and periodic entropy checks to verify codebase coherence. The concept addresses a real problem observed by teams using AI coding assistants—that code can compile and pass tests while slowly eroding internal consistency in ways functional tests alone cannot detect.