Observability for AI Agents: Tracking Performance and Iteration with MLflow

About this session

Agent systems introduce new operational challenges around monitoring, evaluation, and continuous improvement. This workshop explores practical approaches to building observability workflows that help engineering teams track agent performance, analyze quality signals, and iterate on system behavior with greater confidence. Using MLflow as a foundation, attendees will examine how to structure experiments, monitor agent outputs, and implement reproducible evaluation processes for real-world deployments.

The session highlights how teams can leverage Databricks Managed MLflow to support more advanced production use cases.

Participants will leave with hands-on guidance and access to supporting resources that enable them to replicate observability patterns in their own environments.

Speakers

Key takeaways

  • Participants will leave with hands-on guidance and access to supporting resources that enable them to replicate observability patterns in their own environments.

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