engineering · 1 min read
Idempotency in AI Workflows: Why It Matters and How to Get It Right
Re-runs happen - retries, replays, manual reruns. Idempotent workflows survive them gracefully. Here is the pattern Bhogar AI uses everywhere.
BABhogar AI TeamProduct & Engineering
Every production workflow gets re-run somehow - by retries, replays, or human button-pushes. Idempotent workflows handle that gracefully; non-idempotent ones cause duplicate emails, double charges and angry customers.
Why it matters
Idempotency is not the default and cannot be bolted on after the fact. It needs design: idempotency keys, deduplication windows and side-effect isolation.
How Bhogar AI approaches it
Bhogar AI ships idempotency keys at the trigger, node and integration layers. Side-effecting nodes refuse duplicate execution within a configurable window; pure nodes are always re-runnable.
- Per-trigger idempotency keys
- Per-node deduplication windows
- Side-effect isolation and dry-run mode
- Replay-safe DLQ
- Linter warnings for non-idempotent designs
What you get
Customers adopting idempotency-by-default eliminate the entire class of duplicate-action incidents in production.