Regression Identification

Regression identification is the practice of catching regressions — places where something got worse — as early as possible after a change is made. This requires having a baseline to compare against: evaluation scores from before the change, a suite of test cases with known correct behavior, or monitoring dashboards that track key metrics over time. The challenge is that regressions in AI systems can be subtle and distributed: a model update might improve performance on one behavioral dimension while quietly degrading another that wasn’t in the primary evaluation focus. For behavior architects, regression identification is one of the most important defensive practices — without it, improvement efforts can unknowingly introduce new problems that go undetected until users encounter them.