How to Design Fault-Tolerant AI Workflows That Survive Retries

AI workflows powered by large language models frequently fail at an unglamorous point: the retry, where lost connections or undelivered responses can cause duplicate records or repeated actions. A reliable solution involves designing workflows as state machines with clear boundaries rather than linear conversations. Each tool in the flow should declare an idempotency key derived from business logic, normalized inputs, possible side effects, and a durable receipt format to prevent duplicate execution. An orchestrator should consult an execution log before invoking any tool, proceeding only if a successful receipt exists, retrying on transient failure, or pausing for reconciliation when the state is ambiguous. Tracking metrics like retries per step, ambiguous operations, and receipt reuse rates gives a more meaningful picture of workflow reliability than raw token counts.