Incident Commander: AI Incident Response

Incident Commander tackles the slow, fragmented first-response work after a production bug: engineers must connect a failing test or stack trace to the responsible code change, verify a proposed repair, and document what happened while under pressure. The project is designed for on-call engineers and small development teams, and targets the hackathon's debugging and application-maintenance challenges. In the demo, an engineer enters a service, severity, failing test, and raw error log in one dashboard. Incident Commander presents a three-role investigation: Log Analyst identifies the failing function and symptom; Code Historian links the evidence to a suspect Git commit; Fix Writer proposes a focused patch. The combined report surfaces the source file, test, commit, and plain-language explanation, so the engineer can review the reasoning instead of treating the answer as a black box. In this submitted MVP, these agent findings are loaded from structured project files; the deployed backend does not invoke live Bob or watsonx agents at runtime. The FastAPI backend adds context from the sample runbook and README, runs the original sample tests to confirm the seeded bug, then applies the proposed change only to a temporary copy and runs the tests again. It records the actual test result and cleans up the copy, leaving the original sample app untouched. In the submitted recording, the proposed-fix check fails; the dashboard correctly marks the fix unverified and requires human review. The project does not auto-deploy. The workflow then drafts an incident postmortem containing a timeline, impact, root cause, and prevention steps. Its creative value is the complete, explainable loop from incident evidence to change history, a reviewable patch, an independent test outcome, and reusable documentation—not just a code suggestion. By bringing those steps into one guided workspace, it aims to reduce context switching, help engineers reach a trustworthy first read faster, and keep final operational decisions with the human on-call engineer.