Breaking a Vibe-Code Loop: When AI Keeps Fixing the Same Bug

Why this happens in the first place

An AI coding agent fixing a bug is working from the code and error message in front of it, not from a durable memory of every attempt that came before. Fix bug A by changing a function, and that change can break something bug B depends on, something the agent didn't have visible reason to protect while focused on A. Ask it to fix B next, and the fix that undoes A's problem is often the most direct path back, since it's the version of that code the agent has already reasoned through once. Neither fix is wrong in isolation. The loop is a side effect of fixing one visible problem at a time without holding the whole picture, made worse the longer a single conversation runs and the more of its own context gets used up by the back-and-forth itself.

A concrete shape this often takes: a form validates an email field, but rejects a genuinely valid address, so you ask for a fix. The fix loosens the validation, and now an obviously invalid entry gets accepted, so you ask for that to be fixed. The second fix tightens the same regular expression back toward where it started, and the original valid address gets rejected again. Nothing here is a bug in the AI itself; it's a normal consequence of solving a two-sided constraint (reject bad input, accept good input) one side at a time, in a conversation that isn't holding both requirements in view at once.

This is a different problem from a Claude Artifacts context window filling up, covered in this site's own guide on Claude Artifact Token Limits, though a long looping conversation can genuinely make both worse at the same time: more failed attempts means more tokens spent, which brings a context-window limit closer just as the loop itself gets harder to escape.

The signal to stop is earlier than it feels

Two or three prompts on the same bug, each one fixing what you asked and breaking something else, is the actual signal, not five or six. It's tempting to keep going because each individual response looks like real progress, a specific bug got specifically fixed, but the pattern across attempts is what matters, not any single attempt. Set the limit before you start, not while you're in the middle of it: decide upfront that attempt three, if it doesn't resolve things cleanly, means reverting instead of prompting a fourth time.

Rolling back, platform by platform

All three platforms this site covers keep a real version history, not just an undo button for your last keystroke, and rolling back doesn't delete anything you built after the point you're returning to; it stays available and can be reapplied once the loop is resolved.

Lovable: open the chat history, scroll back to the message right before the loop started and click Revert on that version. This restores the code from that point without touching anything else in the conversation.

Bolt.new: use View History or scroll the chat itself, preview a version with the eye icon, then use the restore action once you've confirmed it's the version you want. Reverting to a checkpoint doesn't cost tokens, unlike sending another prompt that might not fix anything.

Base44: open Version History and restore the app's last stable generation state. This is separate from Base44's longer-range data version history for table records, which is tied to specific paid plans; the generation-state restore used here to escape a stuck build is the more immediately relevant feature for this exact problem.

From stuck loop to a working fix

Re-prompting after a revert: what actually breaks the cycle

The prompt that caused the loop was almost always broader than it needed to be, describing a feature or a page rather than the one specific thing that's actually wrong. After reverting, name the exact file or function involved if you can see it in the code, describe the specific behaviour you want instead of the bug you're seeing and leave out everything unrelated to that one problem. A prompt scoped this tightly gives the AI agent far less room to fix your one thing by breaking a different one, since there's less surrounding code in scope for the fix to touch.

When rolling back isn't enough

Some bugs are genuinely harder than a builder's wrapped prompt-to-app pipeline is built to reason through, and no amount of narrower re-prompting inside the same tool will fix that category. At that point, take the specific failing function, the exact error and the relevant part of your schema directly to Claude or Codex, get a working fix there, then bring just that fix back into your project rather than continuing to loop inside the builder or rebuilding the whole thing elsewhere. This is the same narrow, task-level move this site's own comparison of the two covers in more depth, worth reading before you decide which of the two to try.

One more thing worth ruling out before assuming a bug is simply hard: check whether the requirement itself is actually contradictory, the way the email-validation example above was. If you're asking for behaviour that can't logically hold both ways at once, no amount of AI reasoning, inside the builder or outside it, resolves that, because the problem isn't the code, it's the spec. Rereading your own last few prompts for a hidden contradiction is a five-minute check that sometimes saves an hour of prompting a tool to solve something unsolvable as stated.