LLM-as-a-Judge: Score AI Agent Outputs Automatically

LLM-as-a-judge evaluation uses a separate language model to score an AI agent’s final responses against clear, written rubrics, addressing quality gaps that deterministic tests of tool calls or exact outputs may miss. It distinguishes candidate and judge models to reduce self-preference and other biases, and supports pointwise scoring for thresholds, pairwise comparisons for model or prompt selection, and reference-based checks for factual coverage. The guide recommends deterministic validators for exact, safety-critical requirements such as schemas, calculations, permissions, and tool side effects, while using judges for semantic qualities including grounding, completeness, instruction following, tone, and correct use of retrieved information. Using OpenRouter’s Ori Eval, developers can create TypeScript-based tests that combine observable assertions with rubric scoring, run evaluations in CI, compare candidate models, and control costs through sampling and focused criteria. Reliable judging requires observable rubrics, calibration against human-labeled examples, blinded evaluation inputs, version-controlled test data and configurations, awareness of output variance and known biases, and protection of sensitive production data.

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