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Agentic RAG System with MCP and LangChain : Design Modular AI Agents with Retrieval Augmented Generation and LangChain Tools
The future of AI isn’t just about retrieval—it’s about reasoning. Agentic RAG (Retrieval-Augmented Generation) combines powerful large language models with structured tool use, dynamic memory, and feedback-driven adaptation. When paired with frameworks like LangChain, LangGraph, and Modular Cognitive Protocol (MCP), you unlock scalable, explainable, and intelligent agent systems capable of handling complex real-world tasks.
This book focuses on how agentic intelligence, RAG pipelines, multi-agent orchestration, and modular memory architectures converge to build smarter, more reliable AI applications for production.
Written by a seasoned practitioner in the field of AI automation, agent design, and applied LangChain systems, this guide blends real-world engineering expertise with practical, deployable insights. The book reflects up-to-date knowledge based on current tools, open-source best practices, and real use cases—ideal for ML engineers, AI developers, architects, and CTOs navigating the cutting edge of LLM systems.
Agentic RAG System with MCP and LangChain is the definitive guide to building robust, modular, and intelligent AI agents using retrieval-augmented generation pipelines. Going beyond simple retrieval, it introduces a layered design system—Modular Cognitive Protocol (MCP)—that enables agents to plan, observe, act, revise, and collaborate with tool interfaces, vector stores, long-term memory, and feedback loops.
From foundational concepts to advanced production deployment patterns, this book helps you design, build, and scale trustworthy and performant agentic systems.
Architecture deep dives into LangChain, LangGraph, and AutoGen
Full walkthrough of the MCP framework and modular agent design
Best practices for memory (short/long-term), planning, feedback loops
Advanced agent behavior multi-hop reasoning, critic agents, query refinement
Vector store tuning, reranking strategies, latency mitigation, and tool drift handling
Production-ready serverless deployments, CI workflows, observability
Real-world case studies in enterprise search, customer support, research assistants, and industry-specific agents (finance, healthcare, education)
This book is written for machine learning engineers, AI product developers, full-stack engineers, data scientists, and technical founders who want to go beyond plug-and-play LLMs and build modular, goal-driven AI agents using the most reliable and extensible frameworks available today.
Whether you’re transitioning from traditional RAG to agentic intelligence, or leading the architecture of your company’s AI stack—this guide gives you the strategic depth and technical clarity you need.
You don’t need months of trial and error to build scalable, agentic AI systems. In just a few focused weeks, you’ll go from foundational understanding to implementing full-stack agent pipelines, complete with memory, toolchains, and orchestration.
This book focuses on how agentic intelligence, RAG pipelines, multi-agent orchestration, and modular memory architectures converge to build smarter, more reliable AI applications for production.
Written by a seasoned practitioner in the field of AI automation, agent design, and applied LangChain systems, this guide blends real-world engineering expertise with practical, deployable insights. The book reflects up-to-date knowledge based on current tools, open-source best practices, and real use cases—ideal for ML engineers, AI developers, architects, and CTOs navigating the cutting edge of LLM systems.
Agentic RAG System with MCP and LangChain is the definitive guide to building robust, modular, and intelligent AI agents using retrieval-augmented generation pipelines. Going beyond simple retrieval, it introduces a layered design system—Modular Cognitive Protocol (MCP)—that enables agents to plan, observe, act, revise, and collaborate with tool interfaces, vector stores, long-term memory, and feedback loops.
From foundational concepts to advanced production deployment patterns, this book helps you design, build, and scale trustworthy and performant agentic systems.
Architecture deep dives into LangChain, LangGraph, and AutoGen
Full walkthrough of the MCP framework and modular agent design
Best practices for memory (short/long-term), planning, feedback loops
Advanced agent behavior multi-hop reasoning, critic agents, query refinement
Vector store tuning, reranking strategies, latency mitigation, and tool drift handling
Production-ready serverless deployments, CI workflows, observability
Real-world case studies in enterprise search, customer support, research assistants, and industry-specific agents (finance, healthcare, education)
This book is written for machine learning engineers, AI product developers, full-stack engineers, data scientists, and technical founders who want to go beyond plug-and-play LLMs and build modular, goal-driven AI agents using the most reliable and extensible frameworks available today.
Whether you’re transitioning from traditional RAG to agentic intelligence, or leading the architecture of your company’s AI stack—this guide gives you the strategic depth and technical clarity you need.
You don’t need months of trial and error to build scalable, agentic AI systems. In just a few focused weeks, you’ll go from foundational understanding to implementing full-stack agent pipelines, complete with memory, toolchains, and orchestration.
241 pages, Kindle Edition
Published June 27, 2025
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