Automating bug fixes: SWE-Agent implementation

SWE-Agent implementation: where automatic bug fixing breaks

A typo in a validator, a forgotten branch for an empty response, an incorrect error code, a missing test for a boundary value — small defects arrive by the dozen each week.

Individually each takes 10-20 minutes of attention, and that is exactly why they are not put into the plan: the fix looks insignificant against the backdrop of large tasks.

They accumulate for a simple reason — the cost of entry is higher than the fix itself. An engineer needs to interrupt current work, restore context, reproduce the failure, find the place in the code, add a check and conduct a review.

This takes an evening, not ten minutes, so the queue of small fixes grows faster than it is processed.

The bottleneck is in defect triage, not in fixing. Localization, reproduction and hypothesis testing take almost all the time; the fix itself is a few lines.

While a human does these steps, the flow of routine fixes accumulates and pulls attention away from tasks that require architectural work.

An autonomous development agent changes the distribution: it goes through code and tests, finds the cause and proposes a change together with a check, and the engineer confirms the result.

Automatic bug fixing works only where there is a test environment and a clear criterion for fix readiness. SWE-Agent implementation makes sense precisely on such a flow — below we break down how this is covered.

What changes after launching an autonomous agent in development

The first measurable shift is time to closure for a small defect.

A one-line fix used to wait for a free window in the sprint and went to prod with the next release; now the SWE-Agent agent takes the task from the queue, reproduces the failure with a test and brings a ready change to the branch the same day.

The second effect is the share of routine fixes that do not reach engineers at all.

A failed test after refactoring, an unhandled empty response, a lost boundary case, a regression in a module — the agent closes such defects itself, and the team spends time on tasks that need a design decision, not on triaging identical causes.

The review load does not disappear but changes profile.

Changes become small — a dozen lines in one module instead of a hundred across five — but they arrive more often, so we build in the gate right away: a green test suite is mandatory, the scope of edits is limited, and the agent does not touch migrations or environment settings.

Hence the third effect — a whole class of failures goes away, when fixing a small defect touches neighboring code or waits for a release window.

The fix is localized and rolled back with one operation, and service downtime during emergency fixing is reduced from hours to minutes because the build and checks have already been run.

We track this with four numbers: median time to defect closure, share of fixes accepted without rework, number of returns, and share of defects closed in the same cycle where they were found.

Agent operating modes and scenarios for their use

The launch mode determines where the agent gets access to code and how freely it manages the result.

A one-off bug triage and a flow of similar fixes require different degrees of autonomy and different rollback guarantees, so we choose the mode for the nature of the tasks, not for ease of implementation.

The boundaries of autonomy are set not by the mode itself but by policies: which directories the agent may change, how many attempts it gets per task, what happens when it diverges from the tests.

We grant the right to merge without engineer involvement only where checks cover the changed paths and there is one-command rollback.

Implementation stages: from repository analysis to handover to the team

SWE-Agent implementation is primarily work with the repository and test environment, not launching a tool. The agent relies on tests as the correctness criterion: an incomplete or unstable set of checks makes it fix blindly.

So the order of work is: first audit, then the verification environment, and only then a pilot on real tasks.

1. Audit of the codebase and tests. We analyze repository structure, build, module isolation, share of passing checks and coverage of critical paths. Based on the results, we record which areas the agent fixes autonomously and which are closed to changes.
2. Verification environment scheme. We determine what runs for each fix: unit and integration tests, linters, static analysis. The key rule: a fix without a green run does not enter the branch.
3. Environment and rules setup. We build an isolated container with the required services and test data, and describe in the agent instructions the project conventions, boundaries of changes and commit format.
4. Pilot run. We give 10-15 real tasks from the queue — from small defects to edits across several files. We calculate the share of solutions accepted by the reviewer without rework and analyze the reasons for rejections.
5. Verification of fix quality. We separately review rejected solutions: where the agent bypassed tests, fixed the wrong layer, broke backward compatibility. For each class of error, we add a check or refine a rule.
6. Handover of the solution to the team. We hand over the configuration, environment scheme, agent instructions, run log and procedure for triaging disputed fixes. After training, engineers launch tasks and change rules themselves.

What remains with the team: configurations, procedures, documentation

SWE-Agent implementation does not end with launching on one repository — that is a demonstration, not a working environment.

So that the solution lives without our support, we hand over a set of artifacts: using them, your team reproduces the environment from scratch, changes settings and handles incidents without waiting for the contractor.

• Environment configuration — files of the environment in which the agent operates: available tools, time and context volume limits, directories excluded from processing.
• Integration scheme — connection map: which repositories and branches the agent sees, how tasks arrive from the tracker, where the build and test check stands before a fix is accepted.
• Access policies — which keys are issued, to which systems and for what period, where writing is fully closed, how rights are transferred when the responsible person changes.
• Fix rollback procedure — procedure for an unsuccessful change: how the commit is rolled back, who makes the decision, within what time the environment returns to a working state.
• Solution documentation — description of operating logic, settings tree with explanations and typical scenarios: connect a new repository, tighten rules, expand the task area.
• Support procedure — what we monitor, by what signs a degradation in fix quality is visible, how often we review access and update the configuration.

The kit is designed for an engineer who did not participate in the launch: the environment is brought up according to the scheme, access and rollback rules can be read without contacting us, and new repositories are connected according to the described scenario.

Case: how we reduced the flow of small defects in a product team

A product team of eight engineers maintained a mature codebase, and half of the queue consisted of small defects: typos in validation, unhandled edge values, failing checks on outdated data.

Each such fix took 15-20 minutes, but it was reached at best a week later — fixes competed with development tasks.

Metrics before implementation

Each week 45-60 small fixes accumulated, about a third were closed on time. Median time to fix was 6 business days, the oldest defects hung for three weeks. Triaging repetitive requests took up to 20% of the sprint, and some defects were lost during handover between participants.

What was changed and what was achieved

We deployed SWE-Agent in a separate environment: the agent takes a task from the tracker, reproduces the defect in the test environment, prepares a fix and opens a review request with a description of the cause.

The human remained at the gate — without confirmation from the code owner, a fix does not enter the main branch; auto-merge is allowed only for files with stable test coverage.

After a month, the flow of small defects stopped growing, median time to fix dropped to 1-2 days, and the share of rejected agent fixes stabilized at 22% — we either return them for rework or close them manually. Engineers stopped being the bottleneck on repetitive tasks and returned to product development.

What to do if agent implementation has already been started in-house

Launching the agent in-house almost always hits the same thing: it works, but the result cannot be accepted. We break down what we re-check and how to complete the environment based on work already done.

We start with an audit of the current setup. We check whether the environment is reproducible from a clean clone, whether the task is submitted together with a test that fails before the fix and passes after, and what file permissions are limited to.

If checks are run manually or in a shared working directory, any measurements of the result are unreliable — we fix this first.

Typical setup errors are visible almost immediately: too broad a permission scope, because of which the agent touches neighboring modules; no attempt limit, because of which the fix cycle does not converge; tests written by the agent for its own fix — they are green but do not catch regressions.

And almost always there is no set of benchmark tasks, so "it got better" is not confirmed by anything.

Completing the environment is the wrapper around the agent: task preparation, launch isolation, automatic run of checks, review of changes made and a report for each task.

The core itself does not need to be rewritten: it is often enough to narrow permissions and put a check before merging.

The project restart proceeds without losing accumulated work. We save accumulated rules, task formulations and run logs, move the environment to a benchmark set and compare the share of tasks closed on the first run before and after.

We expand the area gradually — one repository and one class of tasks — and only then connect the rest.

Technical doubts: access, security, fix quality

Code fixes mean changing the working repository, so we settle rights and access issues before the first launch, not after. Below are the objections engineers most often raise at the evaluation stage, with substantive answers.

How to limit access to the repository?

We issue the agent a separate service account or token with minimal rights: read code and write only to the working branch, without rights to the main one. Secrets, environment configurations and release branches remain closed.

For a closed environment, we deploy everything inside your perimeter, without calls to external services.

What happens if a fix is incorrect?

The fix goes as a separate commit in an isolated branch, so rollback is a revert or branch deletion, not restoration from a backup. Before merging, the build and tests are run; if at least one fails, the patch does not enter the main branch.

How is test coverage checked?

We run the existing suite plus tests that the agent adds to the change, and look at coverage of the affected files. A change without tests for a new logic branch is not accepted.

Let's discuss the task: how to launch the agent in your environment

The first conversation is about your environment, not our tool: which repositories and branches the agent should see, how the build pipeline is arranged, whether there are tests and linters that will accept a fix, who reviews changes and by what rules. This is enough for us to come back with a scope estimate and implementation plan, not generalities.

• Describe the task: what share of bugs you fix manually, where the team loses time, which fixes repeat from sprint to sprint.
• Provide access to the environment: repository and branching rules, run environment, rights to read build logs.
• Show the test base: which checks must pass before merging into the main branch and where they are insufficient.
• We will agree on review rules: what the agent changes itself and what goes only through a human.
• We start a pilot environment: one service, a limited set of task types, measurable stop criteria.
• Get an implementation plan: stages, roles, control points, what we consider pilot success.
• Scope estimate — based on your metrics, not on the number of files in the repository.
• Handover of documentation to the team: configurations, policies, operating procedures and rollback procedure.

Describe the task in free form — we will come back with an estimate and work plan. Then the decision is yours: launch the agent on one service or first close gaps in tests.