evaluation

To evaluate agent performance, you apply multi-dimensional rubrics that score factual accuracy, tool efficiency, and stateful workflow execution. This approach reliably measures non-deterministic agent quality by validating multi-step reasoning paths rather than relying on a single correctness metric.

The best way to monitor production agents is using a production monitoring sampler with pass-rate alerts. This setup tracks multi-step agent quality over time, enabling you to detect regressions after prompt or model changes and validate tool usage decisions continuously.

LLM-as-judge works by applying configurable prompts to score agent outputs against multi-dimensional rubrics. It scales evaluations for retrieval and reasoning tasks while retaining human review guidance to oversee edge cases and validate citation accuracy.

Yes, you can compare agent configurations by applying complexity-stratified test sets and realistic token budget constraints. This process validates context engineering strategies and tool usage decisions across different agent setups to ensure safer deployments.

You need multi-dimensional rubrics when testing non-deterministic agents that take multiple valid execution paths. Rubrics score factual accuracy, completeness, and source quality, providing reliable regression testing for complex retrieval and multi-agent coordination scenarios.

Evaluating multi-agent coordination requires complexity stratification because agents take varied execution paths that single metrics cannot capture. Stratified test sets isolate reasoning and tool usage failures, ensuring accurate regression testing after context engineering changes.