2d • General Discussion 💬
Where do AI automation projects break most often?
Quick pattern I’m noticing as I learn and read through others’ builds:
Most automation projects don’t fail because the idea is bad — they fail because something breaks in the middle.
For people actively building, where do things usually go sideways first?
- Workflow logic (edge cases, branching, loops)
- Integrations & APIs (auth, limits, weird responses)
- Data quality / structure (JSON, inputs, outputs)
- Human factors (adoption, trust, handoffs)
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Where do AI automation projects break most often?
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