AI agents can reason, collaborate, and act, but what happens when one runs for hours, encounters a failure, waits for human approval, or needs to survive an application restart?

In this session, we’ll explore durable execution patterns for building reliable, stateful agent systems. Through practical demos, we’ll build agents and multi-agent workflows that can maintain state, recover from failures, pause and resume, and coordinate long-running work without starting over.

Examples will use Microsoft Agent Framework and Azure, but the focus is on broadly applicable patterns for durable agents, human-in-the-loop workflows, failure recovery, and observability.

You’ll leave knowing when agents need durable execution and how to design systems that keep working through failures, restarts, delays, and human intervention.