agent-evaluation

Systematically evaluate GenAI agents using MLflow by coordinating datasets, scorers, and tracing to measure accuracy and tool selection. This workflow applies predefined or custom scorers to deliver observable performance improvements for your agents.

Measure GenAI agent tool selection accuracy and answer quality by applying predefined and custom scorers via MLflow genai APIs. Scorers evaluate consistency, accuracy, and cost to provide observable performance improvements across your datasets.

Set up MLflow tracing for evaluating GenAI agents by following the instrumenting-with-mlflow-tracing skill. This prerequisite establishes a reproducible setup, enabling the evaluation execution to accurately track and coordinate your agent's performance.

Yes, you can validate your GenAI agent evaluation pipeline by running a small 3-record dry run. This quick start approach tests your registered scorers, dataset configuration, and tracing setup before executing full MLflow evaluations.

Yes, MLflow evaluation supports custom scorers for GenAI workflows alongside predefined ones. You can define and register custom scorers to specifically measure accuracy, consistency, tool selection, and cost within your agent evaluation execution.

End-to-end GenAI agent evaluation with MLflow requires datasets, scorers, evaluation APIs, and optional tracing. Coordinating these components within your workflow enables reproducible setup, comprehensive result analysis, and observable performance improvements.