Error · AI Automation Society

Error

Most people build automations that work once and break quietly. The real issue is usually not the logic, it's missing error handling on the data input side. I spent way too long debugging a workflow that was "running fine" but silently dropping records because the incoming JSON had inconsistent field names from one API call to the next. Added a simple normalization step before the main logic and it fixed three weeks of phantom failures. One small transform node before you hit your core workflow saves hours of confusion later. Do you build input validation into your flows from the start or add it after something breaks?

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