ResolvAI

Project Description This project is a self-healing production system powered by Codex that automates the full lifecycle of incident resolution. Instead of relying on engineers to manually investigate and fix failures, the system connects existing signals—logs, stack traces, code, and tests—into an intelligent workflow that can detect issues, diagnose root causes, and generate fixes automatically. It introduces a structured, human-in-the-loop approach where incidents trigger automated analysis, and engineers step in only for validation and control. The result is a faster, more reliable, and scalable way to handle production failures, reducing downtime and operational cost. Key Features 1. Automated Incident Detection & Triggering Production failures automatically generate structured incident artifacts (logs, stack traces, context). A watcher system detects these incidents and triggers the resolution workflow instantly. 2. AI-Powered Root Cause Diagnosis Uses Codex with an incident-diagnoser skill to: Analyze logs and stack traces Inspect the codebase Identify the root cause Produces a diagnosis report without modifying code. 3. Human-in-the-Loop Checkpoint Engineers review the diagnosis before any changes are made. Ensures control, trust, and safety in automated workflows. 4. Guided Automated Remediation Codex uses an incident-remediator skill to: Fix the bug Add regression tests Create a branch and commit changes Engineers can refine or guide the process through the IDE. 5. End-to-End Workflow Orchestration Covers the full lifecycle: Detection → Diagnosis → Review → Fix → Delivery Moves beyond code generation to process automation. 6. Extensible Delivery Pipeline Supports future integration with: Automated pull request creation CI/CD validation pipelines Safe deployment gates 7. Scalable Multi-Service Architecture Can expand beyond a single repo to: Multiple services and repositories A knowledge graph capturing dependencies and lineage Enables ecosystem-wide issue resolution. 8. Real Business Impact Faster incident resolution (minutes instead of hours) Up to: 70% reduction in manual investigation 36% lower downtime-related losses 15–35% reduction in IT operations costs