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
- Codebase audit: identify bug types, test infrastructure, repository size.
- LLM selection and ACI configuration: choose a model (GPT-4o, Claude 3.5) and set up the context window.
- Docker container deployment: isolated environment with restricted network access.
- GitHub Actions integration: the agent receives issues, creates branches, commits changes, and opens PRs.
- Testing on 20–50 backlog issues: measure resolution rate and time per bug.
- Production operation: configure monitoring, alerts, and a playbook for the team.
Real case: PHP monolith with 500+ files
For 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 for free.
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.