Build AI agents thatrun your infrastructure
The home for learning Agentic AI for DevOps — CI/CD, Kubernetes, incident response, observability, and cloud ops. Hands-on videos, build-along projects, and learning paths that take you from prompt to production.
Six ways AI is reshaping DevOps
Every pillar comes with videos, projects, and deep dives — so you don't just understand it, you ship it.
AI Agents for Ops
Build autonomous agents that triage alerts, investigate incidents, and take action on your infrastructure — not just chat about it.
Agentic CI/CD
Pipelines that fix their own failing builds, review PRs, and patch security bugs. CI/CD that thinks before it ships.
Kubernetes & IaC
Natural-language kubectl, AI-reviewed Terraform, and agents that diagnose cluster failures and reconcile drift.
Observability & On-Call
AI on-call engineers that read Prometheus, Grafana, and logs — correlate signals, find root cause, and summarize incidents.
DevSecOps
AI-driven security scanning, secret detection, and policy-as-code reviews wired straight into your pull requests.
Cloud & FinOps
Agents that audit AWS spend, right-size resources, and turn cloud cost reports into concrete, automated savings.
Agentic DevOps projects
Each one is a complete, real-world build you can ship to your own stack.
AI SRE — Autonomous Incident Investigator
The agent that finds root cause before you finish your coffee
An AI SRE that watches your alerts, then investigates across metrics, logs, and Kubernetes in an agentic tool-use loop — forming and testing hypotheses until it posts a ranked root-cause analysis with the evidence trail and a suggested fix to Slack. Read-only by design, so a human approves any action.
✓ Mirrors Datadog Bits AI SRE, Resolve AI, Cleric AI
MCP DevOps Agent — your own “goose for ops”
One agent, every tool — via Model Context Protocol
An MCP-native agent that connects to your real infrastructure through MCP servers (kubectl, AWS, GitHub, Prometheus) and executes ops tasks end-to-end with human approval. The exact pattern Block and Uber run internally at massive scale.
✓ Mirrors Block “codename goose”, Uber MCP platform
RAG On-Call Copilot (Slack)
Answers on-call questions from your runbooks, instantly
A retrieval-augmented copilot that ingests your runbooks, wikis, and past incidents, then answers engineers' on-call questions in Slack with cited sources — cutting the “who knows about X?” tax. Uber's version saved ~13,000 engineering hours.
✓ Mirrors Uber Genie, Moveworks
Agentic PR Reviewer
An AI reviewer that actually reads your codebase
A GitHub bot that reviews pull requests like a senior engineer — running shell and Python in a sandbox to navigate the diff, trace symbols, and leave inline comments. Not a single prompt: a real code-execution agent.
✓ Mirrors CodeRabbit, Qodo, Greptile
AI Security Autofix
Find a vulnerability, ship the fix PR automatically
An agent that runs a SAST scanner (Semgrep/CodeQL), feeds each finding plus the surrounding code-flow to an LLM, and generates a verified fix as a pull request — the architecture GitHub ships to millions of repos.
✓ Mirrors GitHub Copilot Autofix, Wiz, Snyk AI
Self-Healing CI/CD Pipeline
A pipeline that fixes its own broken builds
When a GitHub Actions run fails, an agent reads the logs, reproduces the error, writes a fix, and opens a pull request — closing the loop on flaky builds and trivial breakages, with a human merging.
✓ Mirrors GitHub Copilot coding agent, self-healing DevOps pattern
Follow a learning path
Curated sequences that thread videos, projects, and articles into a clear route — beginner to advanced.
Zero → Agentic DevOps
Start from nothing. Understand the production agentic loop, then ship your first two real agents — an on-call copilot and an incident investigator.
- 1readWhat is Agentic AI for DevOps?
- 2projectRAG On-Call Copilot (start here)
- 3projectAI SRE — Incident Investigator
- 4videoWatch: build your first ops agent
AI-Powered CI/CD
Make your pipelines intelligent — agents that review code, patch security bugs, and fix their own failing builds.
AI Platform Engineering
The platform track: Kubernetes, Terraform, and cloud cost agents — then orchestrate them all into one autonomous DevOps team.
- 1projectKubernetes Copilot
- 2projectTerraform Review & Drift Agent
- 3projectAI Cloud Cost / FinOps Agent
- 4projectCapstone: Multi-Agent Ops Platform
Latest tutorials
Full, no-fluff builds you can follow start to finish.
How I Built an Open-Source AI for Home Services (Leads, Quotes & Calls)
AI Picks My Next YouTube Video | Replit
How to Build an AI Shopping Agent with Claude (Full Build)
Claude Code = Stunning Websites FAST
DevOps articles
Long-form, battle-tested write-ups — pulled straight from the blog.
Deploy Three-Tier DevSecOps Kubernetes Project on AWS EKS with ArgoCD, Prometheus, Grafana, Jenkins
Imagine a robust, secure, and scalable web application built with cutting-edge DevSecOps practices — now imagine achieving all that with automation and efficiency. In this guide, we take you…
Deploy Vuejs Application on Google Kubernetes Engine (GKE)- Blue Green Deployment
I will use GitHub Actions to deploy our VueJS project on Google Kubernetes Engine (GKE).
Understanding Kubernetes: A Comprehensive Guide to Container Orchestration
In the realm of modern software development and deployment, managing and orchestrating containerized applications have become essential. Kubernetes, often abbreviated as K8s, has emerged as a leading…
Github Actions CI/CD Pipeline: Deploy Dockeriz Django on AWS EC2 with PostgreSQL, Celery…
If you are not a Medium Member and want to read Free, You can read on My Linkedin .
Deploy Django Application on AWS ECS Fargate using GitHub Actions and Terraform, A Complete CI/CD…
Learn how to deploy a Dockerized Django application on AWS ECS Fargate effortlessly with GitHub Actions and Terraform in this comprehensive tutorial.
DevOps Project: CI/CD Through Git, Jenkins and Tomcat
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