Single-agent demos are convincing. Multi-agent production is where agents collide. When multiple autonomous agents operate on the same infrastructure—each making decisions in parallel, each consuming from its own data stream—the failures aren't caused by any single agent. They're caused by the interactions between them. One agent reroutes traffic. Another changes DNS. A third modifies a firewall rule. Each decision is individually sound. Together, without coherence, they create an incident worse than the one they were fixing.

This talk presents a coherence framework for multi-agent systems built on three mechanisms already proven in the streaming community. Causal consistency preserves cause-and-effect ordering without requiring global synchronization, ensuring that agents who need to coordinate see events in the same causal order while independent agents operate without waiting for each other. Bounded staleness treats data freshness as a controllable per-relationship parameter—an edge agent performing local inference can lag seconds behind the cloud, while one executing a remediation order requires millisecond precision. Shared event streams provide the durable, ordered, replayable log that serves as the system's shared memory, enabling every agent to access the same history and the same versioned state.

This talk connects streaming infrastructure to the hardest unsolved problem in agentic AI: making distributed agents think together while operating apart.