<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This project demonstrates an AI-assisted CI/CD pipeline integrating GitOps, Kubernetes, and automated rollbacks. It targets engineers seeking to automate software delivery by using AI (GPT-4) to fix test failures, reducing Mean Time To Recovery (MTTR) and enabling safer, gradual production deployments via canary releases.

How It Works

A developer push triggers GitHub Actions for security scans and tests. Test failures prompt GPT-4 to analyze errors, generate fixes, and create a PR. On success, a Docker image is built and pushed. Kubernetes manifests are updated, and ArgoCD synchronizes them to the cluster. Argo Rollouts manage canary deployments, gradually shifting traffic with metric-based automated rollback. An MTTR dashboard tracks deployment metrics.

Quick Start & Requirements

• Primary install / run command: Clone repo, npm install (app/), configure .env, set up Kubernetes with ArgoCD/Argo Rollouts, configure GitHub secrets (OPENAI_API_KEY), deploy ArgoCD app. Trigger via git push to demo branch.
• Non-default prerequisites and dependencies: Node.js 18.x+, npm 8.x+, Docker, kubectl, Kubernetes cluster (v1.24+), Argo Rollouts, ArgoCD, Prometheus (optional for metrics), GitHub Actions, OpenAI API key.
• Links: Official Argo Rollouts installation, Official ArgoCD installation.

Highlighted Details

• AI-Assisted Auto Fix: GPT-4 analyzes test failures, generates fixes, and creates PRs, reducing MTTR.
• GitOps with ArgoCD: Manages Kubernetes manifests declaratively for automated synchronization.
• Canary Deployments: Argo Rollouts enable gradual traffic shifting (10%-100%) with metric-based rollback.
• MTTR Dashboard: Tracks deployment metrics and calculates Mean Time To Recovery.
• Security Practices: Includes multi-stage Docker builds, non-root containers, Trivy scanning, RBAC, and health probes.

Maintenance & Community

No specific details on maintainers, community channels, or roadmap are provided in the README.

Licensing & Compatibility

The README does not specify a software license, hindering assessment for commercial use or closed-source linking.

Limitations & Caveats

Requires substantial infrastructure setup (Kubernetes, ArgoCD, Argo Rollouts). Reliance on OpenAI API introduces external dependency, potential costs, and possibility of AI-generated errors. Lack of explicit licensing is a significant adoption blocker.