21h • 🛠️ dev-help
It Worked. So Why Did It Fail?
Here's something I've been thinking about lately…
An AI workflow runs successfully. No errors. The client gets the output.
So why might the automation still be costing them more time than it saves?
I came across G2's 2026 AI Agents report, and one finding caught my attention: 45% of surveyed B2B buyers said reviewing and correcting agent work was eating up more than a quarter of their expected productivity gains.
That got me thinking about what we actually measure when we build automations.
We check whether the workflow ran.
But do we check how much of the output needed fixing? How many retries it took? How much human time was still involved?
A green execution status doesn't necessarily mean a good result.
One metric I'd like to see more builders track is cost per usable result, not just cost per run.
When you build automations for clients, do you measure the quality of the final output?
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It Worked. So Why Did It Fail?
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