AI Automation Works… Until It Hits Real Users

Most automations look perfect in dev.

Then production happens.

What usually breaks first isn’t the AI model or the prompt — it’s:• edge cases you didn’t anticipate• API rate limits or silent failures• schema changes from dependencies• missing retry / fallback logic• unclear agent responsibilities

AI is already powerful enough. System design is the real bottleneck.

When automations are built around real workflows — with validation, retries, clear handoffs, and observability — they actually hold up in production.

Curious to hear from the community 👇What’s the most painful failure you’ve seen after an automation went live?

Shorter version (more casual)

AI automations don’t fail because of AI.They fail because the system around them isn’t built for production.

What usually breaks first in your builds?

If you want, I can: