Where AI automation usually breaks first

I keep seeing AI automation projects fail, and it’s almost never because of the tech.

Most of the time it breaks earlier, when no one can clearly define what a correct output actually is. Everyone wants automation and everyone wants speed, but different people imagine different results.

When that definition isn’t clear, the system starts drifting. The agent fills in gaps, edge cases pile up, and the automation slowly becomes unreliable even though nothing is technically broken.

At this point I don’t build anything until one question is answered: what exactly must be true for this output to be considered correct?

How do you usually handle output definition before building?

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