Autonomous Bug Fixing: SWE-Agent Implementation

Bugs after releases and refactorings consume hours of manual work and slow down the release of new versions. We implement SWE-Agent turnkey: set up the environment, integrate with GitHub Issues, and select a model so the agent autonomously finds and fixes errors. Our team handles the entire cycle—from audit to support—ensuring a reliable solution that accelerates development and reduces the team's workload.

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SWE-Agent: Autonomous Bug Fixing as a Standard

Imagine: you deploy a new release, and CI fails on 20 tests due to a refactoring. Previously, that meant hours of manual searching and fixes. Now SWE-Agent (Princeton NLP) finds all occurrences of the pattern in minutes and fixes them in a single pull request. Average bug fix time: 5 minutes vs 40 minutes for a human, reducing bug-fix costs by 87%. The agent is fully open-source, deploys on your infrastructure, and requires no subscription.

We implement SWE-Agent turnkey: configure the Docker environment, integrate with GitHub Actions, select the optimal LLM, and test on your real issues. Our experience: 5+ years in MLOps and 30+ AI agent deployments. We guarantee a resolution rate of at least 35% on your backlog. Order implementation — first results in 2 weeks.

What Problems Does SWE-Agent Solve?

  • Massive bugs with the same cause. When one refactoring breaks 20 places, the agent finds all occurrences of the pattern and fixes them in one cycle. A human spends hours searching; the agent takes minutes.
  • Test regressions. If you have good test coverage, SWE-Agent finds a fix by receiving feedback from tests. It iterates hypotheses: changes one file, runs tests, analyzes the error, repeats. SWE-Agent based on Claude 3.5 handles bugs 2x better than a human when tests are available.
  • Simple bugs with clear criteria. For example, "when value X, field Y should be disabled." The agent takes the issue, finds the relevant code, and applies the fix. Average time: 3–5 minutes vs 30–40 minutes for a developer.

How We Set Up SWE-Agent: Step by Step

  1. Codebase audit: identify bug types, test infrastructure, repository size.
  2. LLM selection and ACI configuration: choose a model (GPT-4o, Claude 3.5) and set up the context window.
  3. Docker container deployment: isolated environment with restricted network access.
  4. GitHub Actions integration: the agent receives issues, creates branches, commits changes, and opens PRs.
  5. Testing on 20–50 backlog issues: measure resolution rate and time per bug.
  6. Production operation: configure monitoring, alerts, and a playbook for the team.
Real case: PHP monolith with 500+ filesFor a client, we deployed SWE-Agent with Claude 3.5 Sonnet. The problem: the agent got "lost" in the large codebase and couldn't find the root cause of bugs. Solution: we added `find_file` with regex, set the context window to 16K tokens, and split the codebase into modules via `.agentignore`. Result: resolution rate rose from 18% to 41% in one week. The agent began consistently passing industrial tests. We documented the config and handed over access to the client.

What's Included in the Work?

Stage What We Do Result
Analytics Audit codebase, bug types, test infrastructure Report with recommendations for LLM and configuration
Design Select model (GPT-4o / Claude 3.5), configure ACI interface Agent architecture documentation
Implementation Deploy Docker container, integrate with GitHub Actions Working agent in staging environment
Testing Run on 20–50 backlog issues Statistics on resolution rate and time per bug
Deployment Deploy to production, set up monitoring, train the team Agent in production + playbook for developers

Comparison of SWE-Agent with Alternatives

Parameter SWE-Agent Devin GitHub Copilot
Open source Yes No No
Self-hosted Yes No No
Resolution rate (SWE-bench) 38–43% ~30%* Not applicable
Tests required Yes Unknown No
Cost per run $0.03–0.10 (tokens only) $0.50–1.00 $0.10–0.30

*Devin does not publish official metrics — data from independent tests. Per SWE-bench data

How Quickly Will You See Results?

Implementation time: 2 to 3 weeks. First results (fixing 3–5 bugs) appear as early as the second week. Full integration with CI/CD, monitoring, and alerts takes up to a month. Get a consultation — we'll assess your project.

Why Choose Us?

  • 5+ years in AI/ML and MLOps
  • 30+ AI agent implementation projects
  • Certified NLP and Computer Vision engineers
  • Guaranteed resolution rate of at least 35% on your backlog

Contact us — we'll explain exactly how we'll solve your problem.