We spent three weeks debugging a problem that took three hours to fix. Here's what we were doing wrong.

We're building autosquad — a Company OS for AI-native teams. Our stack is heavily AI-assisted. Claude does a significant portion of our development work.
For about three weeks, we were stuck on the same class of problem. Agents stopping mid-workflow. Error messages referencing tools we'd never created. Fix one thing, two others break. The usual nightmare.
Our developers approached it the way they always had. Isolate the failing component. Find the relevant section of code. Ask Claude to explain what's wrong with it. Get an answer. Apply the fix. Watch something else break.
Repeat. For three weeks.
Here's what we eventually figured out: debugging AI-built code with human debugging instincts is the wrong mental model.
When humans write code, bugs are local. Something wrong in function X is usually caused by something near function X. Experienced developers have this assumption so deeply embedded they don't even notice it's an assumption.
AI-built code breaks differently. When you're building across multiple sessions, inconsistencies accumulate at the architecture level — naming conventions that drift between sessions, components that reference each other differently than intended, context that was established in session one that gets implicitly contradicted in session four. The error shows up in one place. The actual cause is somewhere else entirely.
We were treating architecture-level bugs like function-level bugs. That's why nothing was staying fixed.
The person who figured out the solution was the newest member of our team. Three months in, least technical background of anyone we'd hired.
Their suggestion: instead of showing Claude the broken section, show it the whole codebase.
The reasoning was almost childlike in its simplicity: if Claude built this, maybe Claude understands it better as a whole than we do in pieces. Show it everything. Describe what's going wrong. Let it find the cause.
The experienced developers on the team were skeptical. Debugging is about narrowing scope. This was widening it. It felt wrong.
But three weeks of narrowing scope hadn't worked. So we tried widening.
We connected the full repository to Claude, described the failure modes in plain terms, and asked it to analyze the overall architecture and identify what was creating the conditions for the failures — not just what was wrong in the failing section.
Claude gave us a complete chain of dependencies. Not just "this is broken" but "here's the sequence of decisions across sessions that created the conditions for this to break, and here's the order in which to fix them."
We followed the sequence. Everything cleared. Total time from connecting the repo to resolved errors: under three hours.
The insight that stuck with us afterward: the person who solved this had the least attachment to how debugging is supposed to work. That turned out to be the advantage. They didn't have years of "isolate and narrow" instinct telling them the approach was wrong before they tried it.
We've been thinking about this a lot since. In a field changing this fast, there are probably a lot of places where accumulated expertise is actually a liability — where the mental models built in the old environment are the thing blocking you from seeing the solution in the new one.
Mixing your team intentionally — not just for diversity's sake but specifically to include people who haven't yet learned the constraints you've internalized — might be one of the most underrated advantages in AI-native development.
Has anyone else run into this pattern? Where the "wrong" instinct turned out to be right, or where inexperience was actually what unlocked the solution?