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

> Source: <https://dev.to/rajeshbhan82496/how-i-built-an-ai-agent-that-watches-my-logs-and-opens-pull-requests-while-i-sleep-lif>
> Published: 2026-05-29 18:52:08+00:00

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. 💪
```