At 84% agreement, this configuration lands closer to "promising assistive tool" than "drop-in replacement for human QA." That headline number matters, but it's less useful on its own than what the 16 disagreements were made of: 75% of them clustered into three recurring patterns (scope interpretation, criterion thresholds, and evidence standards) rather than spreading evenly across many unrelated causes.

That clustering is the more actionable finding. It means the gap between the judge and the benchmark wasn't primarily a comprehension problem. The model generally understood what happened in the conversation; it was a rubric-application problem: how broadly a criterion's scope should be read, what threshold of adherence counts as "met," and what standard of evidence a claim needs before it's accepted. All three are properties of how the evaluation task is specified, not just of which model is asked to perform it.

For production use, that points toward a specific set of improvements rather than a different model: more explicit rubric boundaries, atomic criteria instead of compound ones, defined evidence standards, explicit handling of conditional language, structured output enforcement and validation, and human review for ambiguous or consequential judgments. The tested configuration shows real potential as an assistive layer for high-volume customer-support QA, but 84% agreement does not support fully autonomous quality scoring without further validation. The central conclusion: LLM-as-a-judge reliability is not solely a model-selection problem. Evaluation and rubric design are part of the AI system.