How I Built an AI Agent That Watches My Logs and Opens Pull Requests While I Sleep 😴🤖

An engineer developed AutoFixer-Agent, an autonomous AI tool built with Python that monitors production server logs in real-time. When it detects a crash or exception, the agent investigates the stack trace, identifies the bug in the codebase, generates a contextual fix using LLMs, and automatically opens a GitHub Pull Request. The project is open-sourced on GitHub.

As a developer, there are few things more anxiety-inducing than the Slack notification sound at 3:00 AM: "Production is down." You groggily open your laptop, pull up the server logs, trace the exception through 5 different files, fix a missing try/catch block, push the hotfix, and try to go back to sleep. I got tired of this. As an engineer obsessed with automation, I decided to build something that solves the problem for me. Enter AutoFixer-Agent . AutoFixer is an autonomous AI agent built with Python that watches your production server logs in real-time. When it detects a crash or an exception, it doesn't just alert you — it investigates the stack trace, finds the exact bug in your codebase, generates a contextual fix using LLMs, and automatically opens a Pull Request on GitHub . You wake up to a PR waiting for review, not a broken production environment. ✅ The architecture is surprisingly simple but immensely powerful: error.log . main , applies the fix locally, runs sanity checks, and pushes a new Pull Request with a detailed explanation of the bug.The hardest part wasn't generating the code — LLMs are great at that now. The hardest part was building the context window . If a generic KeyError happens, the LLM needs to know what dictionary it came from. A naked stack trace is not enough. python Bad prompt hallucination-prone : "Fix this error: KeyError: 'user id'" Good prompt context-aware : "Fix this error: KeyError: 'user id' Surrounding code lines 45-95 of auth/handler.py : ... def process request payload : user = payload 'user id' <-- line 52 ..." To solve this, AutoFixer dynamically pulls in the surrounding 50 lines of code from the file mentioned in the stack trace before sending the prompt to the AI. This gives the model enough context to write a safe , production-ready fix rather than a hallucinated one. Why This Matters We are moving from "AI as a pair programmer" GitHub Copilot to "AI as a DevOps team member." Tools like AutoFixer prove that we can delegate tedious, high-stress tasks — like 3 AM hotfixes — to autonomous systems that handle the boring parts while we sleep. Try it Out I've open-sourced the entire project You can clone it, simulate a crash in your local logs, and watch it generate a GitHub PR in real time. 🔗 GitHub: turfin-logic/autofixer-agent https://github.com/turfin-logic/autofixer-agent If you're into automation, DevSecOps, or AI agents — drop a ⭐ on the repo or contribute. Let's automate the boring and stressful stuff together. 💪