Multi-agent systems shift the evaluation challenge from individual model outputs to the integrity of the coordination layer. When a supervisor agent delegates a task with flawed context, the error propagates and amplifies through the chain, leading to distributed hallucinations that bypass traditional end-to-end testing. Debugging these systems requires treating them as distributed networks rather than isolated large language model (LLM) calls.In this session, Oleksandra Bovkun covers tracing, evaluation, and governance for multi-agent systems and how to ensure that agentic workflows remain reliable, transparent, and secure at scale. This session provides a technical deep dive into solving observability challenges in complex agentic workflows. She explores how to move from black-box testing to a transparent architecture using MLflow tracing.