In this study, we marry automated scoring with automated rubric revision in an iterative, mutually-reinforcing process. One LLM agent scores student responses; a second, the Error Analysis Agent, examines human–engine scoring discrepancies and suggests rubric revisions. We show that this positive feedback loop improves automated scoring performance.