A production alert fires.
You know something is wrong.
But the alert rarely tells you why.
So the debugging process begins.
Check the logs.
Look at traces.
Review recent deployments.
Search the codebase.
Try to reproduce the issue.
Find the root cause.
Write a fix.
Run tests.
Review the changes.
Open a PR.
That entire process can take minutes.
Or hours.
And sometimes the hardest part isn't writing the fix.
It's figuring out what actually happened.
That's what we're trying to solve with FixBugs AI.
We're building an AI debugging agent designed to work through production issues from the initial alert all the way to a validated fix.
The workflow we're working toward looks like this:
Alert
↓
Investigation
↓
Root Cause
↓
Reproduction
↓
Fix
↓
Validation
↓
Pull Request
The interesting part is that debugging requires much more context than simply looking at a piece of code.
You need to understand what the application was doing.
What changed recently.
What the logs are saying.
What the traces are showing.
Which part of the code is involved.
And whether the proposed change actually fixes the underlying problem.
That's why we're building FixBugs around the debugging workflow itself.
Not just code generation.
Not just autocomplete.
Not just another chatbot.
The goal is to have AI investigate the problem, reason about the available context, reproduce the issue when possible, work on a fix, validate the change, and prepare it for an engineer to review.
The engineer stays in control.
AI handles the investigation and repetitive debugging work.
The engineer decides what ships.
That's the idea.
The alert tells you something is wrong.
FixBugs tells you: "I'll fix it."
If you're a developer or SRE, I'm curious:
What's the most time-consuming part of debugging production issues for you?
Top comments (1)
Logs and traces are fine, but what about application runtime context?