Built an agent that drafts sales decks. The reps would not use it until they could see why it chose each slide.

This one taught me that the adoption problem and the model problem are completely separate, and I kept solving the wrong one. The build: a sales team spent hours turning call notes and CRM data into a tailored deck per prospect. I built an agent that did it. Reads the deal context, decides the story, produces a draft sales deck in their template. Technically it worked. The decks were good. And for weeks almost nobody used it. When I finally sat with a rep, the problem was not quality. It was trust. The agent produced a finished deck and the rep had no idea why it picked those slides, dropped that objection, led with that case study. So they did not trust it in front of a prospect, and they went back to building decks by hand where at least they knew every choice was theirs. What actually got it adopted had nothing to do with the model: - The agent now shows its reasoning as a short "here is why this deck" note. Why this framing, why these three proof points, what it left out and why. The rep reads that in thirty seconds and either trusts it or overrides one thing. - Every slide is editable and the agent flags the two or three it was least confident about, so the rep knows exactly where to spend their attention instead of re-checking everything. - It stopped presenting itself as final. It is explicitly "a first draft with reasoning", and that framing alone changed how people treated it. Usage went from almost nobody to most of the team, and I did not touch the generation quality at all. Same decks. The difference was making the agent's decisions visible so a human could stand behind them. The lesson I keep relearning: for anything a person has to put their name on, an agent that shows its reasoning beats a better agent that is a black box. Where have you found that visibility mattered more than raw output quality? And how much reasoning do you surface before it becomes noise?