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AI Automation Reliability Checklist

Use this before an AI-assisted workflow goes live. The goal is not to make every system complicated; it is to make the failure behavior explicit before users discover it for you.

Last updated 2026-08-14

1. Trigger and delivery guarantees

  • Document exactly what starts the workflow.
  • Know whether an event can be delayed, duplicated, or missed.
  • Define how missed events are reconciled.

2. Idempotency and duplicate protection

  • Identify every side effect that must happen only once.
  • Use stable event or business identifiers where possible.
  • Test duplicate webhook and retry scenarios.

3. Authentication lifecycle

  • Document token refresh and credential ownership.
  • Keep secrets outside workflow content and source control.
  • Define what happens when permissions change or access is revoked.

4. Retry and backoff

  • Separate transient failures from permanent validation errors.
  • Use bounded retries and backoff.
  • Define the final escalation path after retries are exhausted.

5. Rate limits and capacity

  • Know provider and SaaS rate limits.
  • Set concurrency and batch sizes intentionally.
  • Plan for spikes rather than average traffic only.

6. AI output validation

  • Parse and validate structured output before downstream actions.
  • Treat model output as untrusted input.
  • Define fallback behavior for malformed or low-confidence responses.

7. Human-review boundaries

  • Identify irreversible, financial, customer-facing, regulated, or privacy-sensitive actions.
  • Make approval states explicit.
  • Preserve enough context for a reviewer to make the decision quickly.

8. Observability

  • Record what ran and why.
  • Capture provider/model identifiers and useful request metadata without leaking secrets.
  • Make failed and pending items easy to find.

9. Recovery and reconciliation

  • Provide a safe way to replay or repair failed items.
  • Create an exception queue or operational view where volume justifies it.
  • Reconcile important records across systems.

10. Documentation and ownership

  • Document architecture, dependencies, credentials, failure modes, and recovery.
  • Assign an owner for the workflow after launch.
  • Keep written specs and async walkthroughs with the system, not only in chat history.

Need this applied to a real system?

I design and review AI automation, integrations, and operational systems with the same production-first approach.