agent-evaluation

To evaluate an LLM agent's output quality, use an MLflow-driven workflow covering tracing setup, dataset creation, scorer registration, and end-to-end evaluation across multiple agent configurations to surface actionable improvement recommendations.

Setting up tracing and registering scorers for GenAI evaluation involves using specific MLflow APIs like mlflow.genai.scorers and mlflow.genai.datasets to build a standard workflow, relying on provided scripts and references for correct operation.

Yes, you can compare multiple agent configurations on a standard task by running end-to-end evaluations with the mlflow.genai.evaluate API, which provides full observability and traceability for output quality, tool use, and cost considerations.

The best way to structure datasets for evaluating GenAI agents is through MLflow's dataset registration API, mlflow.genai.datasets, which ensures your evaluation data is properly formatted for traceable end-to-end agent quality testing.

Yes, the MLflow evaluation workflow supports cost and tool use analysis for LLM agents by applying structured scorers and tracing setup to measure output quality, tool use effectiveness, and cost considerations across different configurations.