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.
- Who This Book Is For
- What You'll Build
- How This Book Is Organized
- A Note on the Rapidly Evolving Landscape
- Acknowledgments to the Community
- The Limits of Traditional Automation
- What AIOps Really Means
- The Infrastructure Context Problem
- Enter MCP and AI Agents
- Real-World Impact and Case Studies
- Why This Matters Now
- What You'll Learn in This Book
- Understanding Large Language Models for Infrastructure Work
- The Context Window and Memory Limitations
- Prompt Engineering for Infrastructure Automation
- AI Capabilities and Limitations in Operations
- Security Considerations for AI-Powered Infrastructure
- Integration Patterns with Existing Tools
- Choosing the Right AI Models and Providers
- Building Reliable AI Systems
- What's Next
- What MCP Is and Why It Matters
- MCP Architecture and Core Concepts
- Protocol Specifications and Communication Patterns
- MCP vs REST APIs and GraphQL
- Real-World MCP Use Cases in DevOps
- The MCP Ecosystem and Tooling
- Security and Trust in MCP Implementation
- Protocol Evolution and Future Directions
- What's Next
- Go Development Environment for MCP
- AWS CLI and SDK Configuration
- AI Tools Integration
- What's Next
- Project Structure and Dependencies
- Basic MCP Protocol Implementation
- AWS SDK Integration
- Resource Discovery and Formatting
- Testing Your MCP Server
- What's Next
- Understanding MCP Tools vs Resources
- Project Structure for Tools
- Implementing MCP Tools
- Extending the AWS Client
- Tool Registration in MCP Server
- Real-World Example: Complete AI-to-Infrastructure Flow
- Chapter Summary
- The Production Infrastructure
- AWS Infrastructure Fundamentals
- Enhanced MCP Server Architecture
- Extended Project Structure
- Core Parameter Structures
- VPC and Networking Tools Implementation
- Auto Scaling Group Tools Implementation
- Application Load Balancer Tools Implementation
- RDS Tools Implementation
- Tool Registration and MCP Integration
- Real-World Example: Complete Production Deployment
- Chapter Summary
- What's Next
- Understanding the Current Architecture's Limitations
- Refactoring Goals and Vision
- Key Refactoring Steps
- Challenges and Solutions
- Preparation for Chapter 9
- Conclusion
- GitHub Copilot and MCP
- VS Code MCP Configuration Deep Dive
- Real-World Scenario: Three-Tier Application with GitHub Copilot
- Understanding Copilot's AI Decision Process
- Advanced Copilot Integration Patterns
- Debugging and Troubleshooting MCP with GitHub Copilot
- Chapter Summary
- From Stateless Tools to Stateful Agents
- AI Agent Foundation
- Agent Architecture Patterns
- Agent Components Deep Dive
- Enterprise Agent Considerations
- What You've Learned
- What's Next
- The Evolution from Automation to Intelligence
- Core Principles of AI Infrastructure Agents
- AI Agent Architecture Overview
- The Agent Execution Flow
- What You Will Build
- The Strategic Advantage
- What's Next
- What is LangChain?
- Why LangChain for Infrastructure Agents?
- LangChain Core Concepts
- Mapping LangChain to Your Agent Architecture
- Agent Patterns in LangChain
- LangChain in Go vs Python
- Why This Matters for Infrastructure Automation
- What You've Learned
- What's Next