Autonomous bug fixer: bugs that fix themselves, with a human on the merge.
An autonomous bug fixing agent is a system that takes a reported bug or a raised exception, reproduces it, finds the cause, writes the fix and the tests that prove it, and opens a pull request for a person to approve. The fixer Paul Rahme built at a US marina-software company runs this loop in production on the client's product, its marina-management software, across a .NET desktop app and its web repositories, with a build gate that retries red builds before anyone is asked to look.
Scale model of the system
Scale model of the autonomous bug fixer: an exception is reproduced, root-caused, fixed with tests, rebuilt, and opened as a pull request for review.
Why the bug backlog never shrinks
Every team carries a backlog of bugs that are real, reproducible, and never quite urgent. An exception fires. Someone files it. It waits behind feature work until a customer escalates, and then a senior engineer spends half a day reproducing it to find the one line that caused it. The fix itself takes twenty minutes.
The work is mechanical: read the trace, find the code, reproduce, patch, test, open a PR. It is also interrupt-driven, which is what makes it expensive. So the backlog grows, fixes ship without tests, and the same exception returns next quarter. Aldenebai's fixer takes that loop off the team and hands a person a pull request to judge. The developer keeps the decision. The machine keeps the grind.
An AI exception auto-fix pipeline, not an autocomplete
From exception to review-ready pull request, step by step
n8n orchestrates, GitHub Actions does the coding, and the build gate decides whether a person ever sees the change.
Intake
A bug or exception is labelled for the agent in GitHub. n8n picks it up, records its state, and queues it. A desktop-exception intake, where a runtime exception arrives with no person filing it, is the planned next step.
Reproduce
A GitHub Actions worker clones the repository and reproduces the failure against current code. Reproducing first stops it patching a symptom it has never seen.
Root cause
The model reads the trace and the code path together and isolates the cause rather than the symptom. The fix targets that, not the line the stack trace points at.
Fix and tests
It writes the change and the tests that prove it, checks its own diff, and commits to a branch. No test, no pull request.
Build gate
Desktop branches must compile on Windows. A red build never reaches a reviewer: the agent reads the compiler output, fixes, and builds again, at most twice. Still red, and the bug is labelled ai-needs-human.
Review-ready PR, human merge
A green build opens the pull request and messages the assignee, carrying the reproduction, root cause, fix, and test run. A person reviews and approves; nothing merges without that. The n8n watchdog shared with the dev agent runs every 30 minutes and flags anything stuck for more than an hour.
What the bug fixer runs on: GitHub Actions, n8n, and a .NET build gate
n8n never writes code; it holds the queue, labels, retries, and notifications. GitHub Actions never chooses the work; it clones, reproduces, fixes, tests, and builds. State lives in the repository as labels and commits, so the audit trail is the codebase itself.
How self-healing software keeps a human on the merge
The agent's only output is a branch and a pull request. It has no path to production that skips a person, and that one rule separates self-healing software with human review from software that quietly edits itself. The reviewer sees a diff, a test run, and the reasoning, with the reproduction and root cause written out above it.
Failure works the same way. The build gate gives the agent two passes to turn a red build green. If it cannot, it stops, labels the bug ai-needs-human, and hands over everything it learned rather than opening a PR that does not compile.
What changes when the backlog works itself
The fixer shares its worker and n8n build gate with the dev agent, whose ticket-to-PR turnaround is about 2× faster than before (the dev agent's figure, rounded from production data at the marina-software client, 2025–2026). The fixer's own volume figures are not published yet; the case study covers the shared pipeline.
Teams with a bug queue, a repository, and a CI runner
This fits engineering teams whose backlog is full of bugs that are understood but unowned: exceptions with a clear trace, regressions with a known trigger. You need a GitHub repository, a build that runs in CI, and a queue where bugs already land. If the regression suite is thin, AI test automation usually comes first.
The first 30 days: the audit picks which class of bug the agent takes first, the pipeline is wired into one repository with the build gate on, and the agent works labelled bugs while your team reviews every PR. Scope widens only when the reviews say it should.
Questions CTOs ask about autonomous bug fixing
What does an autonomous bug fixing agent actually do when a bug comes in?
It reproduces the bug against the current code, traces it to the root cause, writes the fix with tests that prove it, and opens a pull request for a person to review. It does not merge. A red build gets two automatic retry passes, then the bug goes to a human with everything the agent learned attached.
Are the automated bug fix pull requests tested before anyone sees them?
Yes. Every fix ships with tests the agent wrote for that change, run before the PR is opened. Desktop changes must also compile on Windows; a red build triggers the retry loop, not a pull request. The reviewer opens a PR that already builds and passes its own tests.
Can it break production? Who approves the merge?
A person always approves before anything merges. The agent has no path to production that skips a pull request and a human review; its blast radius is a branch. That is what makes this self-healing software with human review rather than software that edits itself unsupervised.
What happens when the agent cannot fix the bug?
It stops and says so. After two failed build-fix passes the bug is labelled ai-needs-human and lands with a person, carrying the reproduction, the suspected cause, and the attempts made. The n8n watchdog shared with the dev agent flags anything stuck for more than an hour, so nothing sits silently.
Does it work with our stack, or only with .NET and GitHub?
The production version runs on GitHub, GitHub Actions, and n8n across a .NET desktop application and web repositories. The pattern works anywhere with a repository, a CI runner, and a bug queue: orchestrate in n8n, code in a CI worker, gate on the build and the tests. Whether your stack fits is what the Automation Audit maps.
Related pages
Hand the backlog to a machine. Keep the merge.
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