What Jarvis actually gets wrong (and why I'm not fixing it yet)
Personal · 2026-08-14
Building · 22 April 2026 · 5 min read
What Jarvis actually gets wrong (and why I'm not fixing it yet)
I sat down to write something useful about Jarvis this week, and my first instinct was to write about all the things it's brilliant at. Because it is, genuinely, brilliant at some stuff. But that felt like marketing, and this blog isn't really the place for that, so instead I want to talk about where it falls over. Feels more honest. More useful too, probably.
So, Jarvis is meant to take the boring admin work off my plate, the scheduling, the follow ups, the "did we actually reply to that email" panic that happens most Tuesday mornings. Most of the time it does exactly that. But there's a specific failure mode I keep hitting, and I think it's worth talking about because it says something about building tools for real work rather than demo work.
Here's the thing: Jarvis is great when the task is bounded. Clear input, clear output, done. Where it struggles is judgment calls, the stuff that requires actually knowing the person you're dealing with, not just the data about them. I had a case last month where a customer email came in that technically matched a "closed, resolved" pattern from three weeks earlier. Jarvis flagged it as low priority. It wasn't. The customer was furious, and the context that made that obvious to a human (tone, timing, the fact they'd emailed twice before with no reply) just wasn't weighted properly.
That's not a bug exactly. It's a limitation of pattern matching dressed up as judgment. And I think a lot of founders building with AI tools hit this same wall and don't talk about it, because admitting your product has a ceiling feels like admitting weakness. I don't think it is. I think it's just true.
Anyway, the fix isn't more automation. That's the bit I keep having to remind myself of. My first reaction, every time, is to throw more rules at the problem. Add another condition, another edge case handler, another layer. But that's how you end up with an unmaintainable mess of if-statements pretending to be intelligence. What's actually worked better is narrowing scope, not widening it. Let Jarvis own the bounded stuff completely, no exceptions, no half measures, and route anything ambiguous to a human fast rather than trying to get the AI to guess better.
There's a trade-off there and I want to be upfront about it. Narrowing scope means less impressive demos. It means when someone asks "can it handle X" the honest answer is sometimes just no, not yet, and maybe not for a while. That's a hard thing to say when you're trying to get people excited about what you're building. But the alternative, oversell now and disappoint later, seems worse to me. Bootstrapped businesses live and die on trust, and trust doesn't survive a tool that confidently gets things wrong.
What I've actually done differently since that email incident: added a simple escalation flag for anything involving repeat contact, regardless of how the pattern matcher scores it. Crude, sure. But crude and reliable beats clever and occasionally catastrophic.
I guess the bigger lesson, if there is one, is that the interesting problems in building this stuff aren't really technical. They're about deciding what you're comfortable being wrong about, and how loudly you tell people about it. Jarvis will get better at judgment calls eventually. Probably. For now I'd rather it knows what it doesn't know, and hands that bit back to me.
Feels like a small thing to admit in a blog post. But I think it's the whole game, honestly.