+300 PAGES OF TECHNICAL CONTENT

Full Table of Contents

15 Chapters + 80 Sections of AI applied to DevOps, SRE and Platform Engineering

Terraform Kubernetes CI/CD Observability FinOps Security GitOps RAG

I Part I: Fundamentals and Context

Understand the real problem and AI fundamentals for DevOps

01

The Real Problem of Modern DevOps

And Why AI Alone Doesn't Solve It

  • 1.1 Tool Chaos and Operational Fragmentation
  • 1.2 DevOps as "Human Glue"
  • 1.3 Why AI Fails Without Standardization
  • 1.4 Platform Engineering as a Prerequisite for AI
  • 1.5 What This Material Is NOT
  • 1.6 What This Material Will Deliver
02

AI Fundamentals for DevOps

LLMs, Claude, MCP and Agent Frameworks

  • 2.1 What Are Large Language Models (LLMs)
  • 2.2 Claude, GPT and Other Models: When to Use Each
  • 2.3 Critical Limitations You Need to Know
  • 2.4 Model Context Protocol (MCP): The Bridge Between LLM and Tools
  • 2.5 Agent Frameworks: LangChain, CrewAI and AutoGen
  • 2.6 Architectural Decisions of This Guide
03

The Modern IDE for DevOps with AI

Cursor, Claude and Environment Configuration

  • 3.1 The IDE as DevOps Command Center
  • 3.2 Cursor: The Native AI IDE for DevOps
  • 3.3 Essential Extensions for DevOps
  • 3.4 Configuration Files: .cursorrules, CLAUDE.md, settings.json
  • 3.5 Starting Your Environment: claude init
  • 3.6 Copilots, Chat and Agents in Cursor
  • 3.7 MCP: The Model Context Protocol
  • 3.8 Common Anti-Patterns in IDE AI Usage
  • 3.9 Agent in Action - Initial Setup in Cursor
  • 3.10 Claude Skills: Specializing AI for Domains
  • 3.11 Building Your Own MCP Server
04

AI Agents for DevOps

From Concept to Practice with Specialized Subagents

  • 4.1 What Is (and Isn't) an AI Agent
  • 4.2 Practical Difference: Prompt, Script, Copilot and Agent
  • 4.3 Anatomy of a DevOps Agent
  • 4.4 Types of Agents in the DevOps World
  • 4.5 MCP Applied to Agents
  • 4.6 Minimum Guardrails for Production
  • 4.7 When NOT to Use Agents
  • 4.8 Anatomy of an Agent Execution (Step-by-Step)
  • 4.9 Anti-Patterns: Where Agents Fail (Real Cases)
  • 4.10 Debugging: When the Agent Makes Mistakes
  • 4.11 Metrics: How to Measure Agent ROI
  • 4.12 Agent in Action — Configuring the DevOps Agent
  • 4.13 Specialized Subagents: Creating an AI Team

II Part II: Infrastructure as Code

Terraform and Kubernetes with AI in practice

05

Terraform with AI

Intelligent Infrastructure in Practice

  • 5.1 Terraform in the Real World (The Silent Pain)
  • 5.2 Where AI Really Helps in Terraform
  • 5.3 MCP Applied to Terraform: The Hybrid Architecture
  • 5.4 Installation Guide: Cursor vs VS Code
  • 5.5 Installing MCPs in Cursor
  • 5.6 Guardrails Configuration: Protecting Production
  • 5.7 The "End-to-End" Flow (Supervised)
  • 5.8 Practical Case - AWS (Avoiding ACL Hallucination)
  • 5.9 Workspace Safety: AI as Environment Guardian
  • 5.10 FinOps: AI as Cost Analyst
  • 5.11 Agent in Action - The Infrastructure Architect
  • 5.12 Terraform MCP Troubleshooting
  • 5.13 Specialized Subagent: terraform-reviewer
06

Kubernetes with AI

Operation, Policies and Intelligent Scaling

  • 6.1 Kubernetes: The Distributed Operating System
  • 6.2 K8sGPT: AI-Assisted Diagnostics
  • 6.3 MCP Kubernetes Server: Setup and Security
  • 6.4 Practical Case: CrashLoopBackOff
  • 6.5 Kyverno with AI: Intelligent Policies
  • 6.6 HPA/VPA with AI: Intelligent Autoscaling
  • 6.7 Argo Rollouts: Canary and Blue-Green with AI
  • 6.8 Subagent: kubernetes-operator
  • 6.12 Hosting MCP Servers on Kubernetes/EKS

III Part III: DevOps Practices with AI

CI/CD, Observability and Troubleshooting

07

CI/CD with AI

Pipelines as Product

  • 7.1 Pipelines as Product
  • 7.2 Intelligent Test Selection (Predictive Test Selection)
  • 7.3 Pipeline Failure Auto-Triage
  • 7.4 Supply Chain Security
  • 7.5 Flakiness: Unstable Tests
  • 7.6 Pipeline Generation with AI
  • 7.7 Pipeline Security
  • 7.8 When NOT to Use AI in CI/CD
  • 7.9 ROI of AI in CI/CD
  • 7.10 Subagent: ci-security-analyst
  • 7.11 Final Configuration
  • 7.12 End-to-End Practical Scenario
  • 7.13 GitHub Actions with Claude Code
  • 7.14 Conclusion
08

Observability and Incidents

From Signal Overload to Intelligent Action

  • 8.1 The Problem of Signal Overload
  • 8.2 AI-Assisted PromQL
  • 8.3 Metrics, Logs and Traces Correlation
  • 8.4 Troubleshooting with ReAct Pattern
  • 8.5 Intelligent Alerts: Noise Reduction
  • 8.6 Subagent: incident-responder

IV Part IV: Specializations

Security, FinOps and Runbook RAG

09

Container and Kubernetes Security

Intelligent Vulnerability Triage

  • 9.1 The Problem of Security at Scale
  • 9.2 Intelligent Vulnerability Triage
  • 9.3 Triage System Architecture
  • 9.4 Automated Prioritization: From Detection to Action
  • 9.5 Secrets Management with AI
  • 9.6 Subagent: security-auditor
10

FinOps: Cost Optimization with AI

Intelligent Cloud Cost Reduction

  • 10.1 The Structural Problem of FinOps
  • 10.2 Intelligent FinOps Architecture
  • 10.3 Implementation: Infracost + AI
  • 10.4 Automated Rightsizing
  • 10.5 Zombie Resource Detection
  • 10.6 FinOps ROI with AI
11

Runbook RAG

Operational Knowledge Instantly Accessible

  • 11.1 The Problem of Distributed Documentation
  • 11.2 Fundamentals: RAG, BM25 and Embeddings
  • 11.3 How RAG Works in Practice
  • 11.4 Production-Grade RAG Architecture
  • 11.5 Hybrid Retrieval: Why It's Mandatory
  • 11.6 Chunking by Operational Sections
  • 11.7 Slack Integration for Quick Access
  • 11.8 Automated Post-Mortem with RAG

V Part V: Governance and Adoption

Security, GitOps and Organizational Adoption

12

Security, Guardrails and Professional Use

Human-in-the-Loop and Responsibility

  • 12.1 Why AI Without Limits Becomes Risk
  • 12.2 Human-in-the-Loop in Practice
  • 12.3 Simple Guardrails That Work
  • 12.4 Responsibility Remains Human
  • 12.5 Security Checklist
13

AI-Assisted GitOps

ArgoCD, Flux and Intelligent Automation

  • 13.1 What is GitOps (Recap)
  • 13.2 Where AI Adds Value in GitOps Flow
  • 13.3 Automated IaC PR Review
  • 13.4 Manifest Generation with Claude
  • 13.5 Drift Detection and Correction
  • 13.6 Integration with ArgoCD and Flux
  • 13.7 Production-Ready Prompts
  • 13.8 Guardrails: What NOT to Automate
14

Governance and Organizational Adoption

How to Scale AI in DevOps at the Enterprise

  • 14.1 The Problem of Uncoordinated Adoption
  • 14.2 Governance Architecture: Repositories and Structure
  • 14.3 CLAUDE.md: The Single Source of Truth
  • 14.4 Adoption Architecture: People and Processes
  • 14.5 Success Metrics

VI Part VI: Conclusion and Future

What's coming and how to prepare

15

Conclusion and the Future of DevOps with AI

FREE PREVIEW

Future Vision and Your Action Plan

  • 15.1 What You've Seen in This Guide
  • 15.2 What Changed (and What Didn't)
  • 15.3 What's Coming in the Next 2-3 Years
  • 15.4 How to Prepare (Practical Actions)
  • 15.5 The Real Risks (Not the Hype)
  • 15.6 The Inconvenient Truth
  • 15.7 Your 6-Month Plan
  • 15.8 The Final Principle
Read Chapter 15 for Free

Ready to master DevOps with AI?

+300 pages of technical content applied to real production

$37 $77
I WANT THE COMPLETE GUIDE

14-day Guarantee | 1 Year Access | Updates Included