When AI Automations Break (And How to Prevent It)
Wambui Ndung'u
AI adds power to automations—and new failure modes. Knowing them ahead of time saves headaches.
Common Failure Points
1. Output format changes The model returns valid content, but in a different structure than your next step expects. One extra comma, a new line, a different key—and the workflow fails.
2. Rate limits and timeouts AI APIs have limits. A sudden spike in volume or a long-running task can trip them. Design for retries and fallbacks.
3. Content drift Prompts get tweaked, or the model updates. Behavior changes subtly. What worked yesterday might not today.
Prevention
- Validate and normalize — Parse AI output into a strict schema before passing it on
- Log and alert — Know when something fails, and what the input looked like
- Fallbacks — If AI fails, route to human review or a safer default
- Test regularly — Especially after prompt or model changes
Recovery
Have a clear escalation path. When automation breaks, someone should get notified. Document how to pause, fix, and resume.