Most agent failures in production are not caused by the model itself. The real issue is data. Your agent might act on information that was true at 6am, even though the situation changed by 9am.

The main difference between demo agents and production agents is data staleness. Batch-refreshed RAG, polling loops, and nightly ETL usually work well, but they fall short when an agent needs to respond to something that happened just twenty minutes ago. In multi-agent systems, a single outdated inference can lead to a wrong answer that other agents rely on, making the problem even bigger before anyone notices.

We'll walk through why event-driven architecture is the right foundation for agents operating on live systems, and cover the engineering decisions that come with it, event-triggered execution, streaming context, agent-to-agent communication patterns, latency budgets, and more.