.png&w=256&q=75)
🤝 Top Collaborators
.png&w=640&q=75)
🤓 Latest Submissions
RootCause — AI Root-Cause Debugger
Modern engineering teams lose an average of 80 minutes per production incident manually correlating stack traces, git blame, and test failures across multiple tools. For teams shipping multiple times a day, this compounds into days of lost productivity per sprint. RootCause is an intelligent debugging assistant built on IBM Bob 2.0 that automates the entire root-cause investigation workflow. When a developer pastes an error message or stack trace, RootCause instantly spins up three parallel subagents that work simultaneously: 1. Trace Agent — parses the production log and extracts precise file, function, and line number from the stack frame. 2. Git Agent — runs live git log commands against the repository to identify the most recent commit that touched the failing code path. 3. Test Agent — replays the failure in a sandbox environment to confirm reproducibility. A fourth Root Cause Synthesizer aggregates all evidence signals, scores each hypothesis by confidence, and selects the leading root cause. It then generates a complete response: a scored hypothesis with evidence chain, a unified diff showing the exact fix, a regression test to prevent recurrence, and a verification result confirming the patch works. The target users are software engineers and on-call SREs dealing with production incidents. They interact with RootCause by pasting any real error — TypeError, CORS, database deadlock, auth failure, OOM, timeout, and 20+ other categories — into a single input field. The system classifies the error type and tailors every output field (hypothesis, fix, test) specifically to that error class. What makes RootCause unique is that every output is derived from the actual input — nothing is hardcoded or templated. The tool rejects non-error inputs entirely, ensuring it only runs on real incidents. The parallel subagent architecture mirrors how a senior engineer would delegate investigation across multiple tools at once, compressing the feedback loop from 80 minutes to under 2 minutes — a 38× speedup.
27 Sep 2026
