agent-evaluation

To evaluate non-deterministic AI agent performance, use multi-dimensional rubrics and LLM-as-judge techniques. This approach scores agent outputs across factual accuracy, completeness, citation accuracy, and tool efficiency without relying on a single correct answer.

The LLM-as-judge methodology uses a language model to score agent outputs against defined criteria. It provides an automated way to assess factual accuracy and tool efficiency when evaluating agents that lack single correct answers.

Design agent test sets by structuring examples across different complexity levels. This structured test set design allows you to comprehensively assess agent performance and iterate on improvements based on quantitative feedback.

Track quality metrics like token usage, tool calls, and model choice to understand key performance drivers. These metrics, combined with multi-dimensional rubrics, provide quantitative feedback for evaluating agent outputs.

Yes, the approach addresses continuous evaluation to measure and improve agent performance over time. It helps avoid common pitfalls in agent testing by providing methodologies for ongoing assessment of non-deterministic outputs.

Agent testing often fails due to common pitfalls like lacking multi-dimensional rubrics or structured test sets. Implementing LLM-as-judge techniques and evaluating across complexity levels helps catch factual accuracy and completeness issues.