---
slug: ai-coding-tool-stuck-loop
title: "How to Recover When Your AI Coding Tool Goes in Circles"
excerpt: "Your AI coding tool is stuck in a loop, suggesting the same broken fix three times. Here are five resets, in order, when Cursor or Lovable stops making progress."
primaryKeyword: "Cursor stuck in loop"
publishedAt: 2026-04-22
readingTimeMin: 7
author: "Robert Boylan"
tags:
  - ai-coding-tools
  - cursor
  - lovable
  - vibe-coding
  - debugging
---

It's prompt fourteen. The same null-pointer error has come back for the third time. Cursor just suggested the exact fix it suggested at prompt nine, which broke the test you're trying to make pass. You feel the loop happening even before the AI re-types the same import statement. You're not making progress; you're making circles.

Every vibe-coder hits this. Cursor stuck in a loop, Lovable rebuilding the same broken layout, Claude Code rewriting the test it just rewrote. The instinct is to keep prompting harder, sharper, more specific. That almost never works. What works is a small set of resets, in order, that pull the AI out of whatever local minimum it's wandered into.

## The signs you're actually stuck

Before you reach for any reset, confirm you're stuck. Real loop signs look like this:

- The AI re-suggests a fix it already tried.
- It "fixes" the bug by changing something unrelated, then changes it back the next prompt.
- Errors keep moving location instead of going away (the bug shifts from line 42 to line 71 to line 18).
- You're rewriting the same prompt with slightly different words and getting near-identical output.
- You've been on the same task for over thirty minutes with no measurable progress.

If two or more of those are happening, the AI isn't going to think harder. It's already tried the obvious thing. Time to break state.

## Reset 1: paste the spec back in

The cheapest reset, and usually the most effective. The AI has likely lost track of what you're actually building. Its context window (the working memory it can see at once) is now full of attempted fixes, error logs, and partial code. The original goal is sitting at the very bottom or has aged out entirely.

Open your spec, the one you wrote before any of this started, and paste it back into the conversation. Add one sentence at the top: "Reminder of what we're building. The current bug is X. Look at this fresh."

The behaviour change is often immediate. With the goal back in view, the AI stops fitting the bug into its last twelve attempts and starts treating it as a problem in the actual app.

If you don't have a spec written down, this reset isn't available to you. That's the larger problem [why your AI code gets worse the longer you work](/blog/why-ai-code-gets-worse-over-time): without an external anchor, the AI's context drifts and there's nothing to reset against.

## Reset 2: open a fresh chat

If pasting the spec doesn't help, the conversation itself is the problem. There's too much accumulated context. Even good models get confused when they're surrounded by ten failed attempts at the same fix.

Open a new chat. Paste the spec. Paste the current state of the broken file (just the file, not the chat history). Describe the bug in two sentences. Ask the AI to look at it cold.

This is a real reset, not a placebo. The AI in a fresh chat doesn't have any memory of the wrong fixes it tried. It's not biased toward "the change I already attempted plus one tweak." Roughly half the time, the cold read produces a different (and correct) fix on the first try.

In Cursor specifically, this means starting a new composer session, not just a new prompt. In Lovable or v0, this is a full new project chat. In Claude Code, it's a fresh session in a new terminal tab.

## Reset 3: rebuild the failing piece in isolation

If two clean attempts can't fix the bug, the bug probably isn't where the AI thinks it is. The error is downstream of something earlier. The AI keeps patching the symptom because the symptom is what you keep showing it.

Take the broken piece out of the app. Make a tiny standalone version, the smallest thing that reproduces the bug. A single component with mock data. A single function called with hand-crafted input. Get it working in isolation, then port the fix back.

This breaks the loop by changing the question. Instead of "fix this bug in this codebase", you're asking "make this small thing work". The AI does much better on the second framing because there's less surface area for assumptions to compound.

You'll also often discover, doing this, that the bug isn't where you thought. The component itself works fine in isolation. The bug is in how something feeds it data. That's a different conversation entirely.

## Reset 4: switch tools

When all three of the above fail, the tool itself is the wrong fit for the task. This is uncomfortable advice in a world of "I built the whole thing in Lovable" tweets, but it's the honest one.

Different tools have different strengths. If you're stuck in Lovable trying to debug a complex auth flow, that's a job for Cursor or Claude Code, where you can read every file, set breakpoints, and reason about the runtime. If you're stuck in Cursor on a UI redesign that should have been pasteable from a Figma reference, drop into v0 or Lovable and let a tool with stronger visual generation do that part.

The hand-off works in both directions. Plenty of indie devs prototype in Lovable, then move to Cursor for surgery. Plenty of developers do the bulk of the build in Claude Code, then open Cursor for visual review. There's a longer write-up of [how Cursor and Claude Code complement each other on serious builds](/blog/cursor-vs-claude-code) that goes into when each tool wins.

If switching tools sounds like overkill for one bug, ask yourself how many prompts you've burned. Twenty prompts of going in circles is more expensive than fifteen minutes of porting a piece of work to a different environment.

## Reset 5: walk away for the night

Genuinely. Close the laptop.

This isn't a self-care line, it's a practical one. Two-thirds of the times I've been stuck in a loop with an AI coding tool, the bug becomes obvious within ten minutes the next morning. Not because the AI got better overnight, but because I came back without the assumption that the bug was where I'd been looking.

If you can't physically walk away (the deadline is real, the issue is blocking), at least do something else for thirty minutes. Make food, go for a walk, read the spec back at a coffee shop. Anything that resets your own context, not just the AI's.

The fact that this works as well as it does should tell you something about where loops actually come from. They're not pure AI failures. They're partly AI, partly accumulated assumptions on your end about where the bug must be. Walking away breaks both at once.

## How to make loops less likely in the first place

A few habits cut the frequency of loops by a lot:

- **Start every non-trivial session with a paste of the spec.** Even when you don't think you need it. The AI uses it to anchor its decisions before any drift starts.
- **Refine the spec when the model keeps drifting.** If you keep having to remind the AI of the same constraint mid-session, write it into the source spec so it's there next time. The [PRD Workshop is built around this loop](/blog/prd-workshop): refine the spec for the next prompt.
- **Don't compound prompts in a long conversation.** After about ten back-and-forth turns, the signal-to-noise ratio drops. Start a fresh chat with the current state instead of asking the same chat to remember everything.
- **Watch for the rebuild-the-same-thing pattern.** That's [a different specific failure mode](/blog/why-ai-keeps-rebuilding-features) that looks similar from the outside but has a different fix.

When loops do happen, the resets above work in the order listed. Don't skip ahead. Reset 1 is cheap, Reset 5 is expensive (in time). You're hunting for the smallest perturbation that breaks the cycle.

The deeper truth most loop-recovery posts skip: AI coding tools don't actually run out of intelligence in a session. They run out of useful context. Every reset above is a way to reload the context with what matters and drop the noise. The reason a fresh chat works isn't that the model woke up smarter. It's that it stopped being asked to fit the answer into a stack of bad attempts.

That's also the reason the spec matters more than the model. A clear spec gives every reset something to reload from. Draftlytic exists for exactly that reload point: when the AI starts to drift, the spec is what you paste back in to bring it home.