Autonomous AI with MCP and RAG: Principles, Architecture, and Building Blocks . - A Developer's Guide to Building LLM Agents with LangChain, OpenAI, a

Autonomous AI with MCP and RAG: Principles, Architecture, and Building Blocks

Unlock the future of AI systems with Autonomous AI with MCP and RAG-a practical, hands-on guide for developers, engineers, and AI architects who want to design intelligent agents using cutting-edge technologies like Model-Context-Protocol (MCP) frameworks and Retrieval-Augmented Generation (RAG).

This book is your complete blueprint for building real-world AI agents that think, plan, and act independently. Learn how to architect and deploy agentic systems using LangChain, OpenAI, AutoGen, CrewAI, ChromaDB, and LlamaIndex. Whether you're developing advanced LLM-based assistants, RAG-enhanced search agents, or collaborative multi-agent workflows, this guide offers clear explanations, code-rich examples, and step-by-step tutorials aligned with 2025's most current practices.

What You'll Learn:

• The fundamentals of autonomous agents and multi-agent systems

• How to design core agent loops with LangChain and OpenAI

• Integrating RAG with vector databases like ChromaDB and Weaviate

• Building and deploying FastAPI-based agent backends using Docker

• Applying ethical, auditable, and secure practices for AI operations

• Real-world architecture diagrams, templates, and deployment patterns

Whether you're a machine learning engineer, Python developer, startup builder, or enterprise architect, this book equips you with the tools and knowledge to create scalable, explainable, and production-ready AI systems.