You have seen this conversation. Maybe you have lived through it this week. It goes something like this.
By that point the tool has confidently "fixed" authentication four times, and the bug is still there. If anything, the codebase around the bug is now more tangled than when you started, and both of you have accumulated a story about what is wrong that no longer matches what is actually happening.
Repeated fix loops are almost never caused by the AI's ability to write code. They are caused by the loop losing contact with evidence.
Symptom, assumption, evidence
Every debugging session has three layers. Naming them helps.
Most repeated-fix loops skip straight from symptom to assumption to code change. That path can look identical to real debugging, right up to the point where the bug does not go away.
The most expensive sentence in AI debugging
There is one sentence that quietly costs more time than any other in the modern debugging loop:
"I found the issue."
It sounds like resolution. It usually is not. It is a summary of a hypothesis. Without evidence attached, it means the tool has found a code path that looks suspicious, or a pattern that resembles a known bug, or a nearby error that could plausibly matter. None of that is proof.
A stronger debugging loop
The loop that gets bugs actually closed has more steps than the fast one, but each step earns its place.
- SYMPTOMWrite down exactly what the user sees, in one plain sentence, with the state they were in when it happened.
- REPRODUCTIONGet back to that same state on purpose. If you cannot reproduce it, you are guessing.
- EVIDENCECapture runtime behaviour. Network. Console. Session state. Which function actually ran.
- CLASSIFICATIONDecide what kind of problem this is (see below) before you decide what code to change.
- FIXChange one thing that the evidence points to. Not five nearby things at once.
- REPRODUCTIONRun the original reproduction again. Only then is the defect closed.
Three kinds of bug, three different responses
When you stop and force yourself to classify a bug before fixing it, most fall into one of three buckets. The right response is different for each.
Notice how much of a fix loop can be saved by asking, before touching code, "which of these three am I looking at?" Most repeated fixes happen because the answer was assumed to be A when it was actually B or C.
The stop-fixing protocol
When you notice a fix loop starting, the highest-value move is often not another prompt. It is a full stop.
- Freeze code changes. No more edits until the next step is done.
- Write the exact symptom, in one sentence, in plain language.
- Reproduce the symptom from a known state, on purpose.
- Capture runtime evidence: network, console, session, actual function calls.
- List every code path capable of producing this behaviour.
- Eliminate the paths the evidence rules out.
- Identify the exact caller or state transition that the evidence points to.
- Fix one proven cause. Not the surrounding area.
- Re-run the original reproduction from a clean state.
- Only now, declare the defect closed.
A fix is not a fix until the original reproduction stops reproducing.
Where Zagvo fits when you are stuck
Zagvo does not read your logs. It does not watch your builder's chat. It works from what you tell it: the task you are on, what you have tried, the error or behaviour you are seeing, and where the confidence starts falling apart.
When you share that, the useful mentor response is not always "here is another prompt." Sometimes it is closer to "this looks like diagnosis, not fixing yet. Here is the evidence worth collecting before you touch the code again." That single reframe is often what breaks the loop.
Your builder writes code. Zagvo helps you decide whether writing more code is even the right next move.
A reset you can use conceptually
Next time your builder is about to "fix" the same bug for the fourth time, try something like this in your own head before you send another instruction.
You are not adding another prompt to the pile. You are stepping out of the pile, briefly, to check what you are actually standing on.
When an AI builder fixes the same bug for the fourth time, the problem is probably not the model. The debugging loop has lost contact with evidence. Give it evidence back.