What is durable execution in AI workflows?

The last piece is durability. A long workflow that loses its place when one call times out is fragile — retrying a failed step only pays off if the retry can pick up close to where things broke, instead of redoing everything that already succeeded.

Durable-execution systems persist the state of a job after every step, so a failure partway through resumes from that saved point instead of starting over. Temporal, one of the more widely used platforms for this, states the property plainly: workflows “automatically capture state at every step, and in the event of failure, can pick up exactly where they left off,” with no lost progress and no manual recovery.^[1] You write the workflow as ordinary code; the platform handles persisting progress and resuming after a crash, a timeout, or a deploy that happened to land mid-run.

This is what makes the retry math on the structural fixes page actually pay off in production. A retry that reruns the whole chain from step one is expensive and can even change the outcome, if any step has a side effect that should not happen twice — sending an email, charging a card. A retry that resumes at the failed step is cheap and, if each step is written to be safely repeatable, safe.

“Safely repeatable” has a standard mechanism behind it: an idempotency key, a unique ID tied to that specific attempt, handed to whatever the step is calling. A payment API that has already processed charge abc-123 once will refuse to process it a second time, even if the workflow engine — unaware the first attempt actually succeeded before the timeout — retries the step. Durable-execution platforms are built around this assumption: they expect steps to get retried and push the responsibility for recognizing a duplicate down to the system being called, rather than trying to guarantee any step only ever fires once.