agent-evaluation

To evaluate LLM agent output quality using MLflow, you use the mlflow.genai.evaluate API alongside registered scorers and datasets to measure tool selection accuracy, answer quality, and cost. This provides a repeatable workflow for QA and pre-deployment validation.

MLflow tracing for LLM agents captures prompts, tool usage, and runtime behavior to enable failure analysis. You need it when diagnosing incorrect responses or cost-inefficient behavior in your GenAI agents during development.

Yes, MLflow genai evaluation requires MLflow version 3.8 or higher and utilizes genai datasets and registered scorers. The Skill provides scripts and templates to discover existing datasets, create new ones, and register scorers for automated evaluation.

The best way to benchmark GenAI agents for tool selection accuracy is using the MLflow-native evaluation workflow. It applies registered scorers via the mlflow.genai.evaluate API to systematically measure and optimize agent behavior across your datasets.

You analyze LLM agent failures by instrumenting your agents with MLflow tracing to capture runtime behavior, then running automated evaluations with provided scripts. This diagnostics approach pinpoints incorrect tool selection and incomplete responses.