Why raising your serverless timeout won't save long jobs

Raising a serverless timeout only delays the risk of failure, since long-running jobs remain vulnerable to platform limits, deploys, outages, retries, and loss of in-memory state, especially for unpredictable AI agents that may make variable numbers of model and tool calls or wait for human input. A $13 billion AI application platform uses a practical rule of moving work expected to run reliably beyond roughly 10 minutes to Inngest, though the more important criterion is whether a job has valuable intermediate work that would be costly to repeat. Inngest supports durable execution by splitting workflows into checkpointed steps that fit within serverless invocation limits, allowing subsequent requests to resume from completed work after failures, deployments, or waits rather than restarting the entire job. Individual tasks that cannot fit within a host timeout must be further divided or moved to a long-lived worker, while pauses for events consume no active runtime. Durable orchestration can protect agent progress and make retries more targeted, but it does not provide execution sandboxes or reduce inference costs, making per-step visibility important for model-selection and cost decisions.

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