Feature
AI Debugger: AI Code Debugger and AI Debugging Tool That Fixes Bugs Automatically
Describe the bug and Agentcode reproduces it, traces it to the root cause, and fixes it. The pull request includes a new test that fails before the fix and passes after, so you can trust the fix is real.
Last updated: August 2026
No card to start · Review-first: the agent never merges
In short
An AI debugger is a tool that locates and fixes defects in code automatically. Agentcode takes a bug description, reproduces the failure, finds the underlying cause rather than patching the symptom, and applies a fix. It then adds a regression test that fails on the old code and passes on the new code, and delivers the whole thing as a reviewable pull request. You review and merge, and the agent never merges on its own.
Reproduce first
The agent recreates the failing behavior before changing anything, so it fixes the bug you actually reported instead of guessing.
Find the root cause
Agentcode traces the problem to its source and fixes that, rather than masking the symptom and leaving the real issue in place.
Proven by a test
Every fix ships with a regression test that fails before and passes after, so the bug stays fixed and the proof lives in the PR.
See it in action
Watch the agent run a task
Pick a task
Plan
- planning
Files changed
Test run
Pull request
You review and merge. Agentcode never merges on its own.
What is an AI debugger?
An AI debugger is a tool that locates and fixes defects in source code with little or no step-by-step direction from you. It is a different job from autocomplete. Autocomplete predicts the next few lines you were going to type anyway. An AI code debugger starts from a symptom, a stack trace, a failing test, or a bug report in plain English, and works backward to the line that is actually wrong.
The useful ones do four things in order, and the order is what separates a real fix from a plausible-looking patch:
- Reproduce. Recreate the failure before touching anything, so the fix targets the bug you reported rather than the one the model guessed at.
- Locate the root cause. Trace the failure to its source instead of suppressing the symptom where it happened to surface.
- Fix. Apply the change, matching the conventions already in the file.
- Prove it. Add a regression test that fails on the old code and passes on the new code.
Step four is the one most tools skip, and it is the only one that gives you evidence. Without a test that failed before the change, "fixed" is an assertion, not a fact.
How to fix bugs automatically without shipping a wrong fix
The risk with automated bug fixing is not that the tool does nothing. It is that it produces something confident and wrong: a null check wrapped around the symptom, an exception swallowed, a test loosened until it passes. Each of those makes the ticket close and the underlying defect survive, usually to resurface somewhere more expensive.
Agentcode is built to make that failure mode visible rather than silent:
| Risk | How the agent handles it |
|---|---|
| Patching the symptom, not the cause | The agent reproduces the failure first and reports the root cause it found in the pull request, so you can judge the diagnosis and not just the diff. |
| Weakening a test to get green | The regression test must fail on the original code and pass on the fixed code. A test that passes both ways proves nothing and does not count. |
| Fix breaks something else | Your whole existing suite runs. If anything regresses, no pull request is opened. |
| Silent merge of a bad fix | Agentcode is review first. It never merges on its own, so a human always sees the fix before it reaches your main branch. |
What you get back is a pull request on GitHub or GitLab containing the reproduction, the root cause explanation, the fix, and the regression test. Reviewing that takes a fraction of the time of debugging from scratch, and you keep the final say.
Where an AI debugging tool helps most
Not every bug is worth handing over. In practice these are the cases where teams get the clearest return:
- Bugs in code nobody currently owns. An inherited service where the original author left is exactly where a human spends two days on orientation and an agent spends twenty minutes reading the repository. This overlaps heavily with refactoring legacy code safely.
- Reproducible failures with a stack trace. The more precise the symptom, the better the diagnosis.
- The long tail of small defects. The tickets that are individually trivial and collectively eat a sprint, which never get prioritized against feature work.
- Flaky tests. Genuinely tedious for a person, and well suited to a tool that can run a suite repeatedly without getting bored.
- Bugs that need a regression test anyway. You were going to write one; the agent writes it as part of the fix.
Where it helps least is the ambiguous report with no reproduction, the "it feels slow sometimes" ticket, and anything where the real question is a product decision rather than a defect. Those still need a person. If your bug volume is spread across many services, the same reasoning applies to running an agent across multiple repositories.
Frequently asked questions
What is an AI debugger?
An AI debugger is a tool that finds and fixes defects in code automatically, starting from a symptom such as a stack trace, a failing test, or a plain-English bug report. Unlike autocomplete, it works backward from the failure to the underlying cause. Agentcode reproduces the bug first, traces the root cause, applies a fix, and adds a regression test that proves the fix is real, all delivered as a reviewable pull request.
Can AI fix bugs automatically?
Yes, for a large share of reproducible bugs, but the fix needs proof before you trust it. The common failure is a confident patch that masks the symptom or a test loosened until it passes. Agentcode guards against that by requiring a regression test that fails on the old code and passes on the new one, running your full existing suite, and never merging on its own.
How is an AI code debugger different from a linter?
A linter checks code against fixed rules and flags patterns it recognizes, without running anything. An AI code debugger works from an actual observed failure, reproduces it, and reasons about why the program behaves the way it does. Linters catch problems you can see in the source; a debugger catches problems that only appear when the code runs. They are complementary, not alternatives.
Does an AI debugging tool work on a large legacy codebase?
That is often where it pays off most. An agent reads the surrounding code before it changes anything, so it does not need the tribal knowledge a new engineer would spend days acquiring. It works from your existing test suite as the safety net, which means the more coverage the legacy code has, the more confident the fix. Low-coverage areas need closer human review.
Is it safe to let AI fix bugs in production code?
It is safe when the tool cannot ship without you. Agentcode is review first by design: every fix arrives as a pull request with the reproduction, the root cause, the diff, and a passing regression test, and it never merges on its own. Your normal approvals, CI, and branch protection all still apply, and the agent never trains on your code.
More of what the agent does
See the whole loop on the how it works page, or compare Agentcode to GitHub Copilot.
Put the agent to work
Describe a task and get a pull request you review and merge.