Why a Cursor debugging tool session stalls
Debugging is a loop: observe, hypothesize, change, observe again. An agent working from your files can do three of those four steps. It cannot observe. Every time you paste a stack trace or describe what you saw on screen, you are manually performing the one step it cannot do, and the quality of the whole loop is capped by how well you describe things.
That is why the third round is usually worse than the first. You are tired, your description gets shorter, and the agent is now reasoning about a summary of a summary.
The three descriptions that mislead an agent
"It does not work"
The agent has to guess which of five plausible failure modes you mean.
It usually picks the one that is easiest to fix, which is rarely yours.
"It throws this error"
An error is a symptom that appears after the real problem, often several layers away.
Fixing the throw site makes the symptom disappear and the defect remain.
"I already tried X"
Without knowing what X actually did to the app, the agent cannot rule anything out.
So it proposes X again, in a different shape.
What closes the loop
Give the agent the observation step instead of performing it yourself. That means something drives the deployed application the way a person would, and reports what happened as evidence rather than as prose.
The shape of that evidence matters more than its volume. What was attempted, what the application actually did, and where the two diverged. An agent can act on that directly. A screenshot and a stack trace still require a human to interpret them, which puts you back in the loop you were trying to leave.
Setup installs a verification skill into Cursor itself, so it knows how to create a case, run it and read the result without you explaining the workflow each time.
Terminal
npm install -g @testsprite/testsprite-cli
testsprite setup
You can do the same from the TestSprite dashboard. Everything else the CLI does is in the CLI repository.
A repro you can hand over beats a description
The single highest-leverage habit is to stop describing the bug and start reproducing it. A written case says which page to open, what to do, and what should be true afterwards. Three lines of that outperform three paragraphs of explanation, because it is unambiguous and because it can be re-run.
It also changes the conversation with the agent. Instead of "the dropdown is broken", the input becomes a run that got to step four and found the wrong option selected. There is nothing left to interpret.
Write the case before the first fix attempt rather than after the third. At that point you still remember exactly what you did to trigger it, which is knowledge that evaporates faster than anyone expects.
A worked example of the loop closing
Take a concrete case. A user reports that editing a saved filter sometimes reverts. You describe it, the agent finds a race between two state updates and proposes a guard. You apply it, try once, it works, you move on. Two days later the report comes back.
What went wrong is not the fix. It is that one manual attempt is a sample of one against an intermittent bug, and intermittent bugs pass a single try roughly as often as they fail. The loop that actually converges looks different: write the sequence down as a case, run it several times, and read the rate. Four failures in ten is a completely different instruction to the agent than "it is broken", because it rules out a whole class of deterministic causes and points at timing.
The second thing that changes is what happens after the fix. The same case runs again, ten times, and either the rate goes to zero or it does not. You now have evidence rather than an impression, and the case stays in the suite so the third report never arrives.
The claim worth distrusting
"I have fixed the issue" is the most expensive sentence in agent-assisted debugging. It is generated from the same reasoning that produced the change, so it carries no independent information. A fix is confirmed when the behavior is observed to be correct, and by definition the agent that wrote it cannot be the one to confirm it.
This is not a criticism of the model. A human who writes a patch and then declares it correct without running anything would get the same response in review.
Make the check survive the session
The case you wrote to reproduce the bug is worth more after the fix than during it. Keep it and run it on every change, so the same defect cannot come back quietly three weeks later.
The GitHub App is a webhook you set up in the TestSprite dashboard. It listens for the deployment event your pipeline already produces, so nothing in your repository changes.
GitHub Actions puts the step inside your own workflow, configured from the terminal.
What changes with a verification step in the loop
TestSprite supplies the observation the loop is missing. Setup installs a verification skill into Cursor itself, so the agent can create a case, run it against your deployed app, and read the result without you relaying anything.
The practical effect on a debugging session is that rounds stop repeating. The agent is no longer reasoning about your recollection of what you saw; it has a record of what was attempted, what the application did, and where the two diverged. A fix it proposes gets confirmed by something other than the reasoning that produced it, which is the only way "I have fixed it" becomes information.
The reproduction also outlives the session. The case that found the bug stays in the suite and runs on every change, so the same defect cannot come back quietly in six weeks.
Can Cursor debug without running the application?
It can reason about code and often find the defect that way, particularly for logic errors with a clear trace. It cannot confirm a fix, and it cannot see state, timing or integration problems that only appear when the app runs.
Why does the same bug come back?
Because the reproduction usually lives in the chat rather than in a test. Once the conversation ends, nothing is checking that behavior any more.
Does this replace the Cursor debugging workflow?
No. It supplies the missing observation step. Cursor still proposes the change, you still decide whether it is reasonable, and the verification tells both of you whether it worked.
How much of my code does it see?
The verification runs against the deployed application through its interface, so it reports on behavior rather than reading your repository.
What if the bug only shows up in production?
Point a run at an environment that reproduces it. If the behavior differs between environments, that difference is usually the actual bug and worth chasing before the code change.
Give the agent eyes, not longer prompts.
Cursor as a debugging tool stalls because nothing in the loop observes the running app. Supply that step, insist on evidence rather than a claim of success, and keep the reproduction as a test so the fix holds.