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It’s 2 AM. Customers are reporting failures.
Production is down.
Your Slack is filling up.
And someone says:
“Give me 10 minutes, I’ll find the root cause.”
Ten minutes becomes 30.
30 becomes 90.
You’re jumping between logs, traces, Git history, source code, deployments, dependencies, and infrastructure, trying to reconstruct what actually happened.
And then, after all that investigation, the actual fix takes two minutes.
I built DebugCause to attack that exact problem.
DebugCause is an AI debugging engineer for production systems that investigates production failures across your engineering stack using multiple specialized AI agents running in parallel.
It connects the dots across:
→ Source code & repositories
→ Logs & traces
→ Git history & deployments
→ Call graphs & dependencies
→ Runtime & infrastructure context
Instead of asking an AI to guess what’s wrong, DebugCause collects and correlates evidence to produce an evidence-backed root cause with a confidence score.
And it doesn’t stop at diagnosis.
DebugCause can generate the code diff, create regression tests, run validation, and prepare the engineering deliverable.
The principle is simple:
No evidence. No fix.
If the evidence isn’t strong enough, DebugCause shouldn’t hallucinate a root cause or generate a risky patch.
The goal:
Turn a ~90-minute production investigation into an evidence-backed diagnosis and validated fix in under 4 minutes.
Building DebugCause meant going beyond simply integrating an LLM. I built around parallel agent orchestration, evidence collection, production integrations, code analysis, and independent validation to make AI useful inside a real engineering workflow.
Not an AI chatbot that talks about your production problems.
An AI engineer that investigates them.
The network for creativity
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