devopssessionsjvr/agentic-ai-demo — explained in plain English
Analysis updated 2026-05-18
Learn how CI/CD, GitOps, and Kubernetes rollouts fit together in one pipeline
See a simulated example of an AI agent proposing a pull request to fix a detected issue
Explore how ArgoCD applies a Git repository's desired state to a live system
Use as a teaching demo for AI-assisted DevOps automation
| devopssessionsjvr/agentic-ai-demo | sunrisefromdark/agentradar | jaccen/awesome-gaussian-skills | |
|---|---|---|---|
| Stars | 47 | 48 | 45 |
| Language | HTML | HTML | HTML |
| Setup difficulty | hard | moderate | easy |
| Complexity | 4/5 | 3/5 | 2/5 |
| Audience | ops devops | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Demonstrates concepts that in practice require a Kubernetes cluster and ArgoCD setup.
This repository is a demo project that simulates a DevOps pipeline with AI-assisted automation. DevOps refers to the set of practices for automating the building, testing, and deployment of software. The demo combines several common DevOps concepts: CI/CD pipelines (automated workflows that build and test code on every change), GitOps using ArgoCD (a practice where the desired state of a system is stored in a Git repository and automatically applied), Kubernetes rollouts (the process of deploying updated container-based applications), and AI-generated pull requests that automatically fix detected problems. The idea is to show how an AI agent can participate in the deployment process by detecting issues and proposing fixes as code changes, rather than requiring a human to manually diagnose and patch failures.
A demo project simulating a DevOps pipeline where an AI agent detects issues and proposes fixes automatically.
Mainly HTML. The stack also includes HTML, Kubernetes, ArgoCD.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
Verify against the repo before relying on details.