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The AIOps Book

From Manual Operations to AI-Powered Infrastructure

Quan Huynh

Master AI-powered infrastructure automation with this hands-on guide to building production-ready MCP servers and AI agents in Go. Transform from manual AWS operations to intelligent automation that understands your environment and makes smart decisions while keeping humans in control.

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About

About

About the Book

Transform your infrastructure operations with AI-powered automation that actually works in production. This comprehensive guide teaches DevOps engineers, SREs, and platform architects how to build intelligent systems using the Model Context Protocol (MCP) and AI agents with Go.

Stop copying configuration files into ChatGPT and start building AI assistants that understand your AWS environment directly. Learn to create MCP servers that give AI systems real-time access to your infrastructure, develop agents that can provision complete environments from natural language descriptions, and implement secure automation workflows that augment human expertise without replacing it.

Written by a practicing DevOps engineer for practitioners, this book focuses on production-ready solutions rather than theoretical concepts. You'll build working systems from day one: MCP servers that integrate with AWS APIs, AI agents that can analyze deployment failures and suggest specific fixes, and complete automation workflows that handle the repetitive tasks while keeping humans in control of critical decisions.

Every chapter includes working Go code examples, real-world scenarios, and practical patterns you can adapt to your environment. The companion GitHub repository provides complete implementations that you can deploy and extend immediately.

Whether you're automating your first infrastructure task with AI or designing enterprise-scale AIOps platforms, this book gives you the knowledge and tools to build systems that are both intelligent and reliable. Learn to leverage AI's pattern recognition and reasoning capabilities while maintaining the security, auditability, and control that production environments require.

The future of infrastructure operations isn't about replacing DevOps engineers with AI—it's about augmenting human expertise with tools that can handle complexity at scale. Start building that future today.

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DevOpsAWSGoArtificial IntelligenceComputers and Programming

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About the Author

Quan Huynh

I am a DevOps Lead at Vikki Digital Bank with extensive experience in designing, building, and managing mission-critical infrastructure for digital banking products on Amazon Web Services (AWS). My professional journey has been deeply rooted in the financial sector, where security, resilience, and scalability are paramount.

As a founder of DevOpsVN, my goal is to empower the tech community by simplifying complex concepts and offering actionable, real-world solutions. Through my writing, I strive to bridge the gap between theory and practice, helping others navigate the ever-evolving landscape of modern technology.

Contents

Table of Contents

Preface

  1. Who This Book Is For
  2. What You’ll Build
  3. How This Book Is Organized
  4. A Note on the Rapidly Evolving Landscape
  5. Acknowledgments to the Community

Part I: Foundation

Chapter 1: The AIOps Revolution

  1. The Limits of Traditional Automation
  2. What AIOps Really Means
  3. The Infrastructure Context Problem
  4. Enter MCP and AI Agents
  5. Real-World Impact and Case Studies
  6. Why This Matters Now
  7. What You’ll Learn in This Book

Chapter 2: AI Fundamentals for DevOps Engineers

  1. Understanding Large Language Models for Infrastructure Work
  2. The Context Window and Memory Limitations
  3. Prompt Engineering for Infrastructure Automation
  4. AI Capabilities and Limitations in Operations
  5. Security Considerations for AI-Powered Infrastructure
  6. Integration Patterns with Existing Tools
  7. Choosing the Right AI Models and Providers
  8. Building Reliable AI Systems
  9. What’s Next

Chapter 3: Model Context Protocol Deep Dive

  1. What MCP Is and Why It Matters
  2. MCP Architecture and Core Concepts
  3. Protocol Specifications and Communication Patterns
  4. MCP vs REST APIs and GraphQL
  5. Real-World MCP Use Cases in DevOps
  6. The MCP Ecosystem and Tooling
  7. Security and Trust in MCP Implementation
  8. Protocol Evolution and Future Directions
  9. What’s Next

Chapter 4: Setting Up Your Development Environment

  1. Go Development Environment for MCP
  2. AWS CLI and SDK Configuration
  3. AI Tools Integration
  4. What’s Next

Chapter 5: Your First MCP Server in Go

  1. Project Structure and Dependencies
  2. Basic MCP Protocol Implementation
  3. AWS SDK Integration
  4. Resource Discovery and Formatting
  5. Testing Your MCP Server
  6. What’s Next

Chapter 6: MCP Tools for Infrastructure Actions

  1. Understanding MCP Tools vs Resources
  2. Project Structure for Tools
  3. Implementing MCP Tools
  4. Extending the AWS Client
  5. Tool Registration in MCP Server
  6. Real-World Example: Complete AI-to-Infrastructure Flow
  7. Chapter Summary

Chapter 7: Advanced AWS Operations

  1. The Production Infrastructure
  2. AWS Infrastructure Fundamentals
  3. Enhanced MCP Server Architecture
  4. Extended Project Structure
  5. Core Parameter Structures
  6. VPC and Networking Tools Implementation
  7. Auto Scaling Group Tools Implementation
  8. Application Load Balancer Tools Implementation
  9. RDS Tools Implementation
  10. Tool Registration and MCP Integration
  11. Real-World Example: Complete Production Deployment
  12. Chapter Summary
  13. What’s Next

Chapter 8: Refactoring for Production-Ready Architecture

  1. Understanding the Current Architecture’s Limitations
  2. Refactoring Goals and Vision
  3. Key Refactoring Steps
  4. Challenges and Solutions
  5. Preparation for Chapter 9
  6. Conclusion

Chapter 9: GitHub Copilot and MCP Integration for Infrastructure Automation

  1. GitHub Copilot and MCP
  2. VS Code MCP Configuration Deep Dive
  3. Real-World Scenario: Three-Tier Application with GitHub Copilot
  4. Understanding Copilot’s AI Decision Process
  5. Advanced Copilot Integration Patterns
  6. Debugging and Troubleshooting MCP with GitHub Copilot
  7. Chapter Summary

Part III: AI Agents for DevOps

Chapter 10: AI Agents Architecture

  1. From Stateless Tools to Stateful Agents
  2. AI Agent Foundation
  3. Agent Architecture Patterns
  4. Agent Components Deep Dive
  5. Enterprise Agent Considerations
  6. What You’ve Learned
  7. What’s Next

Chapter 11: Understand What You Will Build

  1. The Evolution from Automation to Intelligence
  2. Core Principles of AI Infrastructure Agents
  3. AI Agent Architecture Overview
  4. The Agent Execution Flow
  5. What You Will Build
  6. The Strategic Advantage
  7. What’s Next

Chapter 12: Introduction to LangChain Framework

  1. What is LangChain?
  2. Why LangChain for Infrastructure Agents?
  3. LangChain Core Concepts
  4. Mapping LangChain to Your Agent Architecture
  5. Agent Patterns in LangChain
  6. LangChain in Go vs Python
  7. Why This Matters for Infrastructure Automation
  8. What You’ve Learned
  9. What’s Next

Chapter 13: Building the Models Layer

  1. AI Infrastructure Agent Codebase
  2. Understanding the Models Layer
  3. Stage 1: Configuration System
  4. Stage 2: Provider Initialization
  5. Stage 3: Agent Integration
  6. Testing the Models Layer
  7. Handling Provider Differences
  8. What You’ve Built
  9. Next Steps

Chapter 14: Building the Prompts Layer

  1. What You’re Building
  2. Understanding the Prompts Layer
  3. Understanding Retrieval-Augmented Generation (RAG)
  4. Stage 1: Building System Prompts
  5. Stage 2: Context Injection with RAG Implementation
  6. Stage 3: Tool Descriptions and Schemas
  7. What’s Next

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