Why deterministic rule engines beat AI-on-AI checks for catching UI code drift

As AI coding tools like Copilot and Cursor become standard, engineering teams are seeing a surge in subtle UI drift — where generated code quietly diverges from design systems over time. A 2026 Faros AI report found code churn is up 861% from pre-AI baselines, with 31.3% more pull requests merging without any human review. The instinctive fix of using a second AI model to catch the first one's mistakes introduces its own problems: non-deterministic outputs, compounding token costs at CI scale, and a lack of explainability. The author argues that a deterministic rule engine — one that produces the same output for the same input every time — is better suited to gate merges, since failures can be traced to specific, inspectable conditions rather than probability scores. This principle informed the design of ReWeaver, a versioned rule engine that compares design source against code and surfaces drift across nine defined dimensions without relying on model confidence thresholds.