Author(s): Shrashti Singhal

Originally published on Towards AI.

Your agent will die mid-task. The only question is whether the work dies with it. Part ten of a series on production agentic AI.

Here is a story that every team building production agents eventually lives through, usually around month two.

After that opening scenario, the article explains the core principle of durable execution: the logical progress of a task must be treated as persistent data, not as whatever happens to live inside a running process. It breaks down what “agent state” really includes (conversation history, execution cursor, pending external effects, and external references) and then walks through two major durability models—checkpointing (snapshotting state at safe boundaries) and replay/event-sourcing (recording decisions and rerunning workflow code deterministically). The author then covers how durability creates unavoidable retries and therefore demands idempotent or exactly-once “effect” semantics via idempotency keys and intent/result ledgers, plus special handling for irreversible side effects using approval gates and compensation (sagas). It addresses long pauses like weekend approvals by treating “waiting” as persisted state so the process can stop and compute cost goes to zero, while warning about code/schema drift, zombie runs, and reattachment failures. Finally, it compares real-world frameworks and patterns (Temporal, LangGraph, Cloudflare Workflows, DBOS, Restate, Inngest, and DIY approaches), provides a practical one-week startup plan, and concludes by emphasizing that the key production leap is making an agent’s work survive crashes, deploys, evictions, and time.

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Published via Towards AI