How can AI agents recover from partial failures without starting over?

AI agents can recover from partial failures by running each multi-step task as a durable workflow with persisted progress. When a failure occurs, the system should know which steps completed, which step failed, what state was saved, and which operations are safe to retry. Completed work should not be blindly repeated, especially when tool calls write to external systems. Durable execution, replay, idempotency, and compensation logic all help the agent resume from the right point. Diagrid Catalyst emphasizes this model by helping agent workflows resume after failures instead of restarting from the beginning.