Enforcement Feedback Loops

The enforcement system learns. Every violation is data — data that reveals which brand rules are most frequently broken, which prompt templates are most violation-prone, and which generation contexts produce the most drift.

Weekly enforcement report: total assets evaluated, pass rate, violation distribution by tier and by rule, most violated rules (top 5), most violation-prone templates (top 3), trend lines comparing this week to last four weeks. The report drives two types of improvements.

Prompt-level improvements: if a specific template consistently violates a specific rule, the template is the problem. Strengthen the locked elements. Add negative constraints. Tighten the style specification reference. Rule-level improvements: if a rule is violated in more than 40% of evaluations and the violations are borderline (barely exceeding tolerance), the tolerance may be too tight for the generation model's precision. Adjust the tolerance, not the standard — unless the model genuinely cannot achieve the required precision, in which case the standard is aspirational, not enforceable, and should be reclassified.