agent-evaluation-mlflow

MLflow evaluation automates agent scoring and tracking using MLflow 3.x. Configure a tracking server, define your evaluation dataset, and deploy built-in or custom scorers (Safety, Correctness, Relevance, Hallucination, Guidelines) to measure agent quality across experiments and traces.

Quality gates are threshold-based checks that block unsafe or low-quality agent deployments. MLflow safety gates enforce pre-deploy validation, continuous monitoring, canary testing, and drift detection to ensure agents meet safety and correctness standards before reaching production.

Yes. MLflow evaluation includes Groundedness and safety scorers that detect hallucinations and PII leakage. Combine these with custom scorers, prompt-injection resilience checks, and tool safety validation to catch quality issues across your agent lifecycle.

MLflow tracing captures automatic and manual agent execution logs for auditability. Store traces alongside evaluation results and scorer outputs so you can replay, debug, and audit agent decisions during testing and post-deployment monitoring.

Yes. MLflow supports parallel evaluation across multiple scorers—Safety, Correctness, Relevance, Hallucination, Guidelines, ChunkRelevance, and custom scorers. Combine scores into a single evaluation gate to validate agents against diverse quality dimensions.

Pre-deploy gates validate agents before release using batch evaluation datasets. Continuous evaluation monitors live agents for drift and degradation post-deployment. MLflow integrates both to enforce quality standards across the full agent lifecycle.