AI evaluation works best as a pyramid.
At the base, run deterministic checks on every eligible output: schema validity, permissions, required fields, calculations, citations, tool constraints, and latency budgets. They are fast, cheap, and explainable.
The next layer contains task-specific semantic metrics such as retrieval relevance, groundedness, completeness, and instruction following. Model judges can scale these checks, but they need calibrated rubrics and stable versions.
Above that, use human review for uncertain, high-risk, and evaluator-disagreement cases. At the top, connect results to product outcomes: resolution, correction, retention, escalation, or task completion.
The pyramid prevents two mistakes. It avoids paying a model to judge something code can prove, and it avoids pretending automated scores replace expert judgment.
Each layer should route action upward when confidence is insufficient. The result is not one magic quality number. It is an evidence system in which every evaluator does the narrowest job it can perform reliably.