How to Implement Observability to Reduce Agent Sprawl

As AI agents proliferate, organizations are hitting a new kind of architectural debt: agent sprawl. The real issue isn't just fragmented tooling — teams can't trace how agents make decisions, monitor usage across models and tools, or enforce consistent policies once agents reach production.This session shows how observability and control work together to scale agents responsibly. You'll see how MLflow traces every agent decision, evaluates outputs, and closes the feedback loop; how AI Gateway enforces policies and tracks usage across models and tools; and how Unity Catalog ties it all together for unified governance — across low-code builders, high-code frameworks, and MCP integrations.Through a live demo and real customer architectures, you'll leave with the patterns to debug failures, monitor cost and latency, and scale agent systems that are observable, controllable, and production-ready.