Autonomous Ai With Mcp And Rag
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Autonomous AI with MCP and RAG
Author: Nathaniel Crossfield
language: en
Publisher: Independently Published
Release Date: 2025-06-08
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.
Building AI Agents with LLMs, RAG, and Knowledge Graphs
Author: Salvatore Raieli
language: en
Publisher: Packt Publishing Ltd
Release Date: 2025-07-11
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
MCP in Agentic RAG Systems
Author: Darryl Jeffery
language: en
Publisher: Independently Published
Release Date: 2025-05-31
Are you ready to architect smarter, autonomous AI systems that scale effortlessly with your enterprise? "MCP in Agentic RAG Systems: Architect Autonomous Agents for Scalable Automation" is your comprehensive guide to building robust, context-aware AI agents using Model Context Protocol (MCP). This authoritative resource shows you exactly how to leverage MCP servers alongside retrieval-augmented generation (RAG) to deliver intelligent, real-time automation capable of understanding context and scaling to any demand. In this book, you'll discover the transformative power of combining MCP's modular toolsets with advanced RAG architectures. Learn to build intelligent systems that autonomously retrieve relevant knowledge, reason contextually, and make informed decisions with minimal human intervention. Inside this practical guide, you'll find: Fundamentals of MCP and Agentic AI: Master the principles behind modular MCP servers, including event-driven pipelines and context-driven architectures. Advanced Retrieval-Augmented Generation: Implement robust embedding models and vector search to equip your agents with precise, contextually relevant information. Autonomous Agent Design: Understand how to architect AI agents capable of self-reflection, continuous learning, and adaptive context management. Integration Strategies: Combine MCP seamlessly with leading technologies like OpenAI, Claude, and local LLMs to enhance performance and adaptability. Real-world Case Studies: Gain insights from practical applications in finance, healthcare, and retail, demonstrating tangible business outcomes. Future Trends and Innovations: Explore emerging trends such as self-reflective agents, cross-platform collaboration, and multimodal AI capabilities. Whether you're a developer, engineer, or architect, this guide provides actionable strategies to help you build smarter agents, streamline workflows, and deliver automation that scales gracefully. Ready to build the next generation of scalable, intelligent automation? Grab your copy of "MCP in Agentic RAG Systems" today and start architecting autonomous agents that transform your enterprise.