agent-evaluation

You can evaluate GenAI agents using MLflow by leveraging GenAI datasets, scorers, and traces to measure performance. This skill provides template scripts for end-to-end workflows, ensuring you can validate agent tool usage and trace quality across OSS MLflow and Databricks.

MLflow tracing tracks GenAI agent execution paths and tool usage to validate performance. You need it when evaluating complex agent workflows to ensure reproducibility and benchmark scoring accuracy across Databricks and OSS MLflow environments.

Yes, component testing is supported for creating GenAI datasets, integrating tracing, and registering scorers independently. This allows you to validate individual evaluation components before executing full end-to-end GenAI agent workflows in MLflow.

Yes, the evaluation workflow supports both Databricks and OSS MLflow environments. It provides references and best practices for reproducibility and benchmarking, ensuring you can implement and validate agent evaluations across both platforms.

The best way to benchmark GenAI agent performance with MLflow is using provided automation scripts and references for reproducibility. This skill helps you implement end-to-end evaluation workflows, analyzing tool usage and scorer performance through standardized templates.