How does replay help AI workflows recover after failure?

Replay helps an AI workflow recover by reconstructing the completed path from durable workflow history instead of asking the system to redo every prior step. In a well-designed durable workflow, completed activities can be replayed from stored results, while only the failed or unfinished step needs to run again. This is especially important for AI agents because LLM output may be non-deterministic, and rerunning earlier reasoning steps can lead to a different path. Diagrid Catalyst uses this durable replay model as part of its recovery and observability story for production agents.