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5 things I got wrong about building an AI agent tool

I've been building KuberAgent for a few months now. Looking back, most of my early time went into solving problems that didn't actually matter. Sharing this in case it saves someone else the same detour.

  1. I thought more autonomy = more value.

My first instinct was to make the agent do as much as possible without asking questions. Turns out users don't want a black box that runs off and does 10 things they want something that does 2 things well and tells them what it's doing. I was optimizing for the wrong axis entirely.

  1. I underestimated how much people fear being embarrassed by automation.

Nobody wants to be the person who let an AI agent send a bad email, mess up a client deliverable, or post something wrong publicly. That fear is bigger than most feature gaps. I didn't design for it until users told me directly.

  1. I built for the "power user" first.

I kept adding configuration options — custom prompts, advanced settings, toggles for every behavior. Made sense to me as the builder. But most users just wanted a default that worked well out of the box. The config options I was proud of, almost nobody touched.

  1. I assumed onboarding was a UI problem.

I redesigned the onboarding flow twice, thinking better screens would fix drop-off. The actual issue was that people didn't understand what the agent could and couldn't do before they started. A one-line expectation-setting sentence fixed more than either redesign did.

  1. I waited too long to talk to users.

Classic mistake, I know it's said constantly, but I still did it anyway. I spent weeks building based on what I assumed people wanted. The first real conversation I had reshaped my roadmap more than a month of solo building did.

None of these were fun to realize. But each one saved me from building further in the wrong direction.

If you're building something agent-based right now has trust/fear of automation come up for your users too, or is that specific to my case?

posted to Icon for group AI Tools
AI Tools
on July 15, 2026
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    Point 4 is the one that'll save other people the most time, "onboarding is a UI problem" is such a common misdiagnosis, when usually it's an expectations problem that no amount of redesign fixes, exactly as you found.

    Did you get to measure drop-off before and after adding that expectation-setting line, or was it more qualitative signals like support tickets that told you it worked?

    1. 1

      Honestly, more qualitative than a clean before/after. I didn't have proper funnel tracking set up at the time , so I can't give you a hard drop-off percentage.

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        That actually tracks with the "waited too long to talk to users" point, same instinct, just applied to metrics instead of users: build the thing, worry about measuring it later.

        If you want a lightweight starting point rather than nothing, dropping 3-4 basic events into PostHog or Mixpanel's free tier usually takes under an hour and gives you enough to spot drop-off patterns going forward, even without historical data.

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