Cascading Failures in Multi-Agent Systems

Evaluating multi-agent systems requires a fundamental shift in perspective: we are no longer just testing model outputs, but the integrity of the coordination layer. Instead of just getting one bad response, we get a complex palette of hidden—and not-so-hidden—issues like poisoned shared memory, sub-optimal decision patterns, and distributed hallucinations.In this session, we’ll cover how to identify these glitches with MLflow observability, how to move from passive monitoring to active intervention, and which Databricks components can actually prevent the chaos from happening in the first place. The goal of this talk isn't to give you a recipe for every possible disaster, but to broaden your thinking in a world where agents are becoming more autonomous—and significantly more creative with their mistakes.