Full Table of Contents

I Part I: Fundamentals and Context

Understand the real problem and AI fundamentals for DevOps

The Silent Revolution of DevOps

And Why You Need to Lead It

1.1 3 AM. 47 Alerts.
1.2 DevOps as Human Glue
1.3 Why AI Fails Without Standardization
1.4 See the Transformation
1.5 What Makes This Guide Different
1.6 Critical Pain Points
1.7 What You Will Build
1.8 The Journey Begins

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

The Modern IDE for DevOps with AI

VS Code, Claude Code CLI and Environment Configuration

3.1 The IDE as DevOps Command Center
3.2 VS Code + Claude Code: The Recommended Setup 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 the IDE
3.7 MCP: The Model Context Protocol
3.8 Common Anti-Patterns in IDE AI Usage
3.9 Agent in Action - Initial IDE Setup
3.10 Claude Skills: Complete Guide — Structure, Patterns, Testing, Distribution & Troubleshooting
3.11 Building Your Own MCP Server
3.12 Context7: Real-Time Documentation via MCP
3.13 Opus 4.6: Adaptive Thinking, Compaction API and Fast Mode
3.14 Hooks: Automation & Guardrails in Claude Code
3.15 AWS MCP Servers — Complete Ecosystem for Cloud DevOps

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
4.14 Agent Teams: Multiple Agents Working in Parallel

II Part II: Infrastructure as Code

Terraform and Kubernetes with AI in practice

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: VS Code, Cursor and Claude Code CLI
5.5 Installing MCPs in the IDE
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
5.14 Agent Teams: Multi-Module Infrastructure Refactoring
5.15 Beyond Terraform — CloudFormation & CDK with AWS MCP

Kubernetes with AI

Operation, Policies and Intelligent Scaling

6.1 Kubernetes: The Distributed Operating System
6.2 K8sGPT: From CLI to Continuous Monitoring
6.3 MCP for Kubernetes: Giving "Eyes" to the Agent in the IDE
6.4 MCP Installation Verification
6.5 Real End-to-End Case: Payment Service Down
6.6 Policies as Code: Kyverno + AI
6.7 Intelligent Autoscaling: KEDA + AI
6.8 Intelligent HPA/VPA: AI-Guided Configuration
6.9 Deployment Strategies: Canary and Blue-Green with AI
6.10 Specialized Subagent: k8s-troubleshoot
6.12 Hosting MCP Servers on Kubernetes/EKS
6.13 AI-Assisted EKS/Kubernetes Upgrade
6.14 Agent Teams: Multi-Agent EKS Upgrade Validation
6.11 Chapter Conclusion

III Part III: DevOps Practices with AI

CI/CD, Observability and Troubleshooting

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 Claude Code SDK — Programmatic Automation
7.15 Conclusion

Observability and Incidents

From Signal Overload to Intelligent Action

8.1 The Problem of Signal Overload
8.2 Logs, Metrics and Traces Correlation
8.3 AI Support for On-Call
8.4 MTTR Reduction with Assisted Decision
8.5 Resource Forecasting with Prophet
8.6 Real Incident Practical Case
8.7 Incident Agent Architecture
8.8 Persona and Subagent Configuration
8.9 Limitations and When NOT to Use AI
8.10 Agent Teams: Automated War Room for P1 Incidents
8.11 CloudWatch MCP Server — Native AWS Observability
8.12 Cost Explorer MCP — Intelligent FinOps with AI
8.13 Grafana MCP + OpenTelemetry — Multi-Cloud Observability
8.14 Chapter Conclusion

IV Part IV: Specializations

Security, FinOps and Runbook RAG

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 Specialized Subagent: security-auditor
9.7 Agent Configuration (.cursorrules)
9.8 Limitations and When NOT to Use AI
9.9 Chapter Conclusion

FinOps: Cost Optimization with AI

Intelligent Cloud Cost Reduction

10.1 The Structural Problem of FinOps
10.2 Intelligent FinOps Architecture
10.3 Implementation: Essential Components
10.4 FinOps ROI with AI
10.5 Conclusion

Runbook RAG

Operational Knowledge Instantly Accessible

11.1 The Problem of Distributed Documentation
11.2 Fundamentals: RAG, BM25 and Embeddings
11.3 Stack Selection: Detailed Comparison (2025-2026)
11.4 Step-by-Step Implementation with Qdrant + LlamaIndex
11.5 Integrating Multiple Sources: Jira, GitHub, Confluence, Slack
11.6 Creating an Agent/MCP that Uses RAG (Anti-Hallucination)
11.7 Slack Integration for Quick Access
11.8 RAG Metrics and Evaluation
11.9 Limitations and Troubleshooting
11.10 Conclusion and Implementation Checklist

V Part V: Governance and Adoption

Security, GitOps and Organizational Adoption

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

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 Where to Place Prompts and How to Automate
13.9 Guardrails: What NOT to Automate

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

VI Part VI: Conclusion and Future

What's coming and how to prepare

Conclusion and the Future of DevOps with AI

FREE PREVIEW

Future Vision and Your Action Plan

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

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