I’ve been thinking about a support automation story I read recently. A team replaced a simple rules engine with an LLM classifier. The model was around 92% accurate. Sounds good. Until you realize that at 100 tickets a day, that’s roughly 8 mistakes every day. The interesting part wasn’t the accuracy though. It was what happened when the model was wrong. Nobody could explain why a ticket was classified a certain way. Nobody could point to a specific rule. Nobody could quickly fix the behavior. The team eventually started reviewing every classification manually. The automation was still running, but the trust was gone. That got me thinking. A lot of discussion around AI agents focuses on making decisions better. Better prompts. Better models. Better reasoning. But I rarely see people discussing what happens after the decision. How is the decision verified? How is it audited? How do you know an action should actually be executed? Maybe the biggest challenge for AI agents isn’t getting from 92% to 96%. Maybe it’s building systems that people can trust when things go wrong. Curious how others are thinking about this. submitted by /u/docybo [link] [留言]

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