Evaluating agent performance with MLflow scoring and tracing

MLflow provides built-in tools to evaluate and trace generative AI agents, letting engineers measure quality metrics and inspect step-by-step execution of LLM chains and agents. MLflow Evaluate supports LLM-as-a-judge and custom metrics to score agent outputs, while MLflow Tracing captures inputs, outputs, and intermediate steps (retrievals, tool calls, LLM calls) for debugging and analysis. Together, tracing and evaluation help engineers identify quality issues, latency bottlenecks, and unexpected agent behavior before and after deployment.

1 · Learn the must-know

• 1mlflow.evaluate() can score agent/model outputs using built-in metrics (e.g., toxicity, ARI, exact match) or custom/LLM-as-a-judge metrics defined via mlflow.metrics.genai.
• 2MLflow Tracing automatically logs a hierarchical trace of an agent's execution, including retriever calls, tool invocations, and LLM calls, viewable in the MLflow UI.
• 3Traces can be logged manually via the fluent API (mlflow.start_span) or automatically through autologging integrations for supported libraries like LangChain.
• 4Each trace captures inputs, outputs, latency, and metadata for every span, enabling root-cause analysis of failures or slow responses in multi-step agent chains.
• 5Evaluation results and traces are logged as MLflow runs/artifacts, so they can be compared across model versions or prompt/config changes for regression testing.
• 6LLM-as-a-judge metrics require configuring a judge model (e.g., via an endpoint) and can be customized with grading criteria to assess qualities like relevance, groundedness, or safety that are hard to measure with heuristic metrics.

2 · Check your understanding