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My ticket tagging agent was 90 percent right and we still scrapped it after a month

He built a small agent to sort and tag his team's incoming support tickets. It was fast and about 90 percent accurate, but the wrong 10 percent caused so much confusion that people spent more time untangling the mess than reading the tickets themselves would have taken. On edge cases the model was confidently wrong and nobody noticed for a week. They scrapped it after a month and went back to tagging by hand.

This one went wrong. Read what happened so you don’t hit the same problem.

How it works

  1. Build a tagging agent
  2. Run it on the team's tickets
  3. Wrong tags on about 10 percent cause confusion
  4. Confidently wrong edge cases go unnoticed for a week
  5. Scrap it after a month and return to manual triage

Why it’s worth knowing

90 percent right was not close to good enough, because the wrong tags cost more time than the right ones saved.

Where it’s from

Summarized from a public post on reddit.com. See the original ↗

Upgraded prompt

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3The check when it’s done
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