You’ve seen this movie before, maybe on a different tool.
Something breaks. You tell the agent. It “fixes” it. It’s still broken — or now something else is broken too. You say “still not working” or click retry. It tries again, slightly differently, still wrong. Twenty minutes and a dozen credits later, you’re staring at the same bug, except now there are two bugs, and you can’t remember what the app looked like before you started.
If you searched “ai keeps making bugs worse,” “lovable try to fix loop,” “ai coding agent stuck loop,” or anything in that family, you’re not describing a bug in one specific tool. You’re describing the failure mode of vibe coding itself. Every major AI coding tool does this, because the mechanism that causes it isn’t tool-specific — it’s structural.
This post is the map. It explains why the loop happens across tools, then gives you the sequence that reliably breaks it, no matter which agent you’re using.
The pattern, confirmed across tools
Strip away the branding and the loop looks identical everywhere:
- Lovable’s “Try to Fix” button is the most literal version of this — users report it burning $15 to $70+ in credits on a single incident, clicking the same button against the same unresolved bug because there’s no other obvious move. We cover the targeting failure behind this specifically in why Lovable edits the wrong component.
- Replit’s Agent has been reported alternating between two different failed approaches — fix A breaks thing B, fix for B breaks thing A again — with users describing $70-a-night credit burn from sessions that never converge. See Replit’s app-breaks-on-deploy pattern and Replit agent rewriting code it wasn’t asked to touch for the two most common shapes this takes.
- Cursor agents given broad, under-scoped tasks will repeat the same move — including quietly reverting a fix you already confirmed was working. That specific version is in Cursor’s silent revert bug.
- Claude Code sessions that run long enough will re-read files and overwrite their own recent changes, effectively fixing something, forgetting it fixed it, and “fixing” it back to broken. This gets worse under the rate-limit pressure covered in Claude Code 529s and rate limits, where retries pile up faster than the agent can reason about them.
- Base44 has a version with no retry button at all — it just hangs on “Thinking” once enough contradictory instructions have piled up in one session. That’s Base44 stuck on Thinking, and it often shows up alongside Base44 auth breaking after deploy, because both come from the same root cause: too much unresolved context, not enough scoping.
- Lovable + Supabase data issues are a slower-burning version of the same thing — a fix that looks successful in the preview but never actually reaches the database, so each follow-up “it’s still not saving” adds noise without adding the information that would let the agent find the real problem. Covered in Lovable Supabase data not saving.
Different products, different UI for the retry button, same underlying failure. That’s the tell that this isn’t a bug in any one tool — it’s a property of how these agents work.
Why it happens (the mechanism, not the vibes)
Four things compound, in roughly this order:
1. Context degrades as the session grows. Every message, every diff, every “no not that” adds tokens to what the agent has to hold in its head. Early in a session, the agent’s model of your app is sharp. Twenty exchanges in, it’s working from a blurred average of everything that’s happened — including the wrong turns.
2. Vague retries add noise, not information. “Still broken.” “Try again.” “That’s not it.” Each of these feels like feedback to you, but to the agent it’s an instruction with zero new signal — it doesn’t say what’s still broken, in which file, or what “not it” means. The agent has to guess again, usually by varying its previous guess slightly, which is why loops often alternate between two or three near-identical wrong fixes instead of converging.
3. The agent can’t see your app the way you can. You’re looking at a rendered page and clicking through a flow. The agent is reasoning over code and your text description of what you saw. When those two don’t line up — and they often don’t, because describing a UI bug in words is lossy — the agent optimizes for a plausible-sounding fix to the wrong problem.
4. Every failed attempt becomes part of the context the next attempt reasons over. This is the part that turns “the agent got it wrong once” into a loop. Attempt two doesn’t start fresh — it starts from a context window that now includes attempt one’s wrong diff, your frustrated correction, and the agent’s own explanation of what it thought it fixed. That’s more noise for attempt three to inherit. The loop isn’t random; it’s compounding.
Put together: a long session degrades the agent’s precision right as your prompts (understandably) get vaguer and more frustrated, at the exact moment the agent most needs a clean, specific signal to break out.
How to break the cycle
None of these require a different tool. They work on Lovable, Replit, Cursor, Claude Code, and Base44 alike, because they target the mechanism, not the product.
1. Stop feeding the loop. The rule of thumb: never say “try again” or “still broken” twice in a row. If the first retry didn’t work, the problem isn’t that the agent needs one more attempt — it’s that your instruction didn’t carry enough information to change its answer. Sending the same low-information prompt a third time just adds more noise for it to reason over.
2. Revert to the last known-good state before re-attempting. Undo the failed fix (or the last few, if the loop has been running a while) before you try again. Don’t stack a new attempt on top of a broken one — that’s how one bug becomes two or three. Most tools have a version history or revert action; use it before you type anything else.
3. Restate the problem from scratch, in a fresh session, with precise scope. This is the single highest-leverage move. Open a new conversation if you can, and write a prompt that specifies:
- The exact file or component (the literal name, not a visual description)
- What’s wrong, stated as a concrete, observable fact
- What “right” looks like, stated just as concretely
“It’s still broken” becomes “In CheckoutForm.tsx, the submit button calls handleSubmit but the loading state never resets on error, so the button stays disabled after a failed request — it should re-enable and show the error message.” That’s a fix request the agent can act on without guessing.
4. One change at a time. Bundling “also fix the header while you’re at it” into a bug-fix prompt gives the agent two problems to reason about with one context budget, and that’s how a clean fix for problem A quietly reintroduces problem B. Ship the fix, verify it, then move to the next thing.
5. Verify against the actual app, not the agent’s claim of success. Agents are frequently confident and wrong about whether their own fix worked, because they’re often checking their reasoning, not your running app. Reload the preview, click through the actual flow, check the actual database row. Confirm it yourself before you consider the loop closed.
That five-step sequence — stop, revert, restate with scope, isolate, verify — is the same sequence underneath every tool-specific fix guide linked above. The tool changes; the discipline doesn’t.
If you do this often, there’s a tool for it
Step 3 above — restate the problem with the exact file, the exact wrong state, and the exact right state — is the part people skip, mostly because typing it out precisely every time is tedious, especially mid-frustration when the loop has already burned twenty minutes. If you find yourself doing this constantly, it’s worth knowing that scoping step is exactly what Traileo automates: you point at the actual broken element in your running app, and it generates a fix request with the component, the observed wrong state, and the intended right state already structured — the same shape as the manual version above, minus the retyping. Not a replacement for any of the five steps; just a faster way to do step 3 correctly instead of skipping it under pressure, which is usually the moment the loop starts.
The takeaway
Creation is the easy part now — every one of these tools is genuinely good at generating a first version of your app. Iteration is where they struggle, because a fix request needs precision that a frustrated “still broken” doesn’t carry, and the agent has no way to see your app the way you do. The loop isn’t a sign you’re using the tool wrong at a deep level. It’s a sign the prompt needs to carry more information than “try again” — every time, on every tool.
Next time you’re three attempts into a fix that isn’t landing, stop, revert, and restate from scratch with exact scope. It feels slower. It’s faster than the loop.