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Long-running software work, including data pipelines, AI agents, approval processes, and webhook-driven tasks, is vulnerable to crashes, resource exhaustion, and external delays when it is designed like a short-lived request. Drawing on a healthtech pipeline that repeatedly failed overnight because its progress existed largely in memory, the article argues that reliable systems must be designed to recover from failures in the middle of execution rather than restart from the beginning. It recommends dividing work into short, durable steps that save results as they complete, using memoization and idempotency keys to safely resume work without repeating side effects, and storing orchestration state outside individual worker processes. It also advises releasing compute resources during waits, distributing large datasets across many isolated chunks instead of a single massive job, and applying flow control to protect dependent systems. The author presents Inngest as a platform that provides these durable-execution capabilities, allowing runs to span long periods while individual steps remain short, retryable, and resilient to deployments or worker failures.

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