Where AI automation breaks (and how to catch it first)
Real data is messier than demo data. The gap between the two is where most automation projects quietly fail.
Most “AI automation” that needs fixing didn't fail dramatically. It failed quietly: a support agent gave confidently wrong answers for three weeks before anyone noticed the resolution rate had dropped.
Problem 1: no eval set
If you can't point to a set of real inputs and expected outputs you test against before every change, you're flying blind. This is the single highest-leverage thing missing from most builds that need rescuing.
Problem 2: silent failure instead of a visible one
A workflow that errors loudly gets fixed fast. A workflow that returns a plausible-sounding wrong answer can run for months. Build for the second case: log everything, flag low-confidence outputs, make it easy to spot-check.
Problem 3: nobody owns it after launch
Automation isn't a one-time project: inputs drift, tools change their APIs, edge cases appear. This is the real argument for a retainer, not a sales tactic: someone needs to be watching.
The fix is boring, on purpose
Logging, eval sets, review gates, and a monthly look at what changed. None of it is exciting. All of it is what keeps an automation running a year later, not just past the demo.
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