Original Summary

Something we ran into while building an AI agent for a pretty dry B2B workflow (document review/scoring type stuff): the outputs that got the best reaction in demos were not the outputs that got trusted in production. In a demo, "wow, it caught that" lands great. Day 30 in production, a user who got burned once by a confidently wrong output stops trusting anything the agent says, even when it's right 95% of the time. The trust doesn't come back linearly either - it takes way more correct calls to rebuild it than it took wrong calls to break it. So we ended up deliberately dialing back the "impressive" framing. Fewer confident one-line verdicts, more "here's what I found and here's my reasoning, you decide." Fewer flashy summarizations, more structured breakdowns a human can quickly sanity-check. It demos worse. Users like it more. Curious if others building AI features into existing workflows have hit the same thing - did you find a point where you had to trade "impressive" for "trustworthy," and how did you know it was the right trade? Also curious if anyone's found a good way to measure trust calibration itself, beyond just watching retention/usage drop off after a bad output.   submitted by   /u/nordic_ash [link]   [comments]

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  • 信息来源：Reddit · SaaS
  • 发布时间：2026/9/22 19:36:37