I built an autonomous error-fixing agent as a solo founder — launching tomorrow
I'm Mason, and I built BugStack as an internal tool for my other startup, FuelScout. I was a solo founder running a product with real users, and production errors kept hitting while I was away. By the time I caught them, users had already churned.
The fix was almost always a few lines of code. So I asked myself, if I can read a stack trace, pull the relevant files, and write a fix, why can't an AI agent do the same thing end to end?
That question turned into a 3-week build sprint. BugStack now captures production errors, pulls context from your GitHub repo, generates a minimal AI fix, validates it, runs your CI, and auto-deploys, all without waking you up.
A few things I learned building it:
→ Context is everything. Sending just the error to an LLM gives you garbage. BugStack fetches the erroring file, its imports (2 levels deep), type definitions, and test files before generating anything. That's what makes the fixes actually work.
→ Confidence gating is non-negotiable. Not every AI fix should ship automatically. BugStack rates every fix and only auto-merges when confidence is high AND CI passes. Everything else becomes a PR for human review.
→ Code style matters more than you think. If the fix uses different indentation or quote style than the rest of the codebase, developers reject it on sight — even if it's correct. BugStack detects and matches your exact conventions.
Launching on Product Hunt tomorrow. If you're curious, follow the page so you get notified, and I'd genuinely love to hear: would you trust an AI agent to deploy fixes to your production app? What would it take to earn that trust?
Replies
The confidence gating architecture is exactly right — but it surfaces a deeper question: who defines what counts as "high confidence," and can that threshold adapt over time per-codebase?
Your current gate (confidence score + CI pass) is a good proxy. But different teams have very different risk tolerances. A startup with 3 users and fast iteration cycles might be fine with a lower confidence threshold than a fintech with 50k users and a compliance requirement. The gate shouldn't be a fixed value — it should be a function of context that the team can tune.
The code style matching point resonates a lot. Rejection-on-sight is a real trust-breaking failure mode. A fix that's technically correct but looks "foreign" signals to a developer that the agent doesn't actually understand their codebase — it just pattern-matched. That first impression matters disproportionately in whether the team keeps trusting the system or overrides it every time.
I'm building Aitinery (AI travel planner, pre-launch) and we hit the same architectural question: the same confidence gate logic applies. "High confidence" in a travel itinerary looks very different for a solo backpacker vs. a family of 5 with a non-negotiable dietary restriction. The gate has to be per-user, not global.
Curious: does BugStack's confidence score improve over time per-repo — learning which fix patterns that codebase historically accepts vs. rejects? Or is it stateless per-error?
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