Where a Trae debugging tool loop plateaus
An agent debugging from source can read, reason and edit. It cannot observe. You are supplying the observation, in prose, from memory, and that channel is narrow. It carries what you noticed and drops what you did not think was relevant, which is frequently the part that matters.
The plateau arrives when the agent has exhausted the hypotheses that your description supports. More rounds do not help, because the input has not improved.
Three kinds of bug this hits hardest
Timing
Something renders before the data arrives, or a request completes after the component is gone.
Impossible to see in a diff, obvious when you watch it happen.
Leftover state
The first action works and the second does not, because something was not reset.
A reproduction that starts fresh will never show it.
Environment mismatch
The code is right and the environment disagrees: a missing variable, stale cache, different data.
The agent keeps fixing code that was never broken.
Add the observation step
Instead of describing what you saw, have something drive the deployed application and report what happened. The result the agent receives is then a record rather than a recollection: what was attempted, what the app did, where those diverged.
Setup installs that capability into the agent, so it can produce and read its own evidence rather than waiting for you to relay it.
Terminal
npm install -g @testsprite/testsprite-cli
testsprite setup
The same setup is available in the TestSprite dashboard if you would rather not install anything locally. The rest of the CLI surface is in the CLI repository.
Two questions that break a plateau
Does it fail from a clean start? If yes it is a logic problem. If only on the second attempt, it is leftover state, and every code-level hypothesis so far has been aimed at the wrong thing.
Does it fail in a different environment? If the same build behaves differently in two places, stop editing code. The difference between the environments is the bug.
Both are cheap to answer and both redirect the whole session when the answer is surprising.
Keep the reproduction after the fix
The case you built to see the bug is the check that stops it returning. Run it on every change rather than deleting it with the conversation.
The trigger matters more than the mechanism. Pointing it at your deployment event means every change is checked without anyone deciding to; the GitHub App does that from the dashboard, and a GitHub Actions step does it from inside your workflow.
Why does the agent keep proposing similar fixes?
Because the input has not changed. Without new observations it can only re-explore the same hypothesis space, phrased differently.
Should I paste more logs?
More signal helps, and logs describe what the code decided rather than what the user experienced. A run against the real application covers the gap that logs leave.
Is this specific to Trae?
No. Any agent debugging from source has the same blind spot. The workflow differs, the missing step is identical.
What if I cannot reproduce it reliably?
Then intermittency is the finding. Run the same case several times and compare. A flow that fails one time in four is a timing or state problem, which narrows the search considerably.
Does the fix need to be verified separately?
Yes. A fix proposed and confirmed by the same reasoning carries no independent evidence. Something has to observe the corrected behavior.
The loop needs an observation, not another round.
A Trae debugging tool session plateaus when the agent runs out of hypotheses your description can support. Give it real observations of the running app, ask whether the failure survives a clean start and a different environment, and keep the reproduction as a permanent check.