Agent State, Checkpointing, and Durable Execution

A long-running agent is a distributed workflow, so the answers come from durable execution rather than LLM folklore: model state as an explicit serializable object updated by reducers, checkpoint after every step so a crash resumes instead of replaying, and give every side-effecting tool an idempotency key plus a durable record written before the call. Explicit state also buys time-travel debugging, human pause-and-resume, and forking a run. AI, ML, and GenAI interviews probe it because the thing that kills agents in production is not reasoning quality, it is a process restart halfway through a 40-step task.