Why AI Agents Need Workflow-Level Idempotency to Prevent Duplicate Actions

AI agents are more prone to creating duplicate real-world actions than traditional apps because they operate under uncertainty and retry by default after timeouts, worker restarts, or planner loops. Unlike a human who consciously decides whether to resend a request, an agent receiving an ambiguous HTTP failure often cannot determine if the original action — such as a payment or email — was already completed. Effective idempotency for AI agents requires a layered approach combining stable operation IDs, API-level idempotency keys, and read-before-write checks, rather than relying on any single control. Event deduplication is also critical after writes, as webhook replays and queue redeliveries can reopen closed work if processed event IDs are not stored. Common failure points in production include TTL gaps, regenerated keys, weak audit logs, and missing compensating actions for partial successes.