GradeAgentOps: A Verification-First Framework for Evidence-Anchored LLM Exam Grading

Large Language Models (LLMs) are increasingly used for rubric-based assessment, but reliable automated grading requires more than a single prompt-response step. This study presents GradeAgentOps, a verification-first framework for evidence-anchored LLM exam grading that combines strict grading contracts, deterministic verification and canonicalization, bounded semantic repair, optional memory modules, and provenance-aware logging. The framework is evaluated on a university-level dataset of 1000 short open-ended exam responses from 100 students across 10 questions, with annotations from two independent expert human graders. A controlled ablation protocol compares six configurations, including a rubric-only baseline and progressively stronger variants with repair and memory augmentation. Human–human agreement provides the reference context, with overall ICC(2,1) = 0.678 and QWK = 0.678 between the two graders. Using one expert grader as the operational reference, the FULL configuration achieves the strongest model-human agreement (MAE = 1.935, RMSE = 2.500, QWK = 0.652, Within ±2 = 0.667), with the consistency memory configuration with repair (C1) emerging as the closest alternative. The gains are not uniform: improvements are more pronounced on argumentative items than on technical ones, and pairwise comparisons show that consistency memory contributes more strongly than rubric memory alone. The stronger configurations also produce cleaner accepted outputs, with lower rates of evidence-related postprocess issues, although at a moderate operational cost. Overall, within this controlled single-course evaluation setting, the results show that reliable automated grading benefits not only from model capability, but also from pipeline design choices that promote verification, evidential coherence, and stable grading behavior.