Agentic Ai System Using Rag
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Agentic Ai System Using Rag
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Author : BRIAN. PITMAN
language : en
Publisher: Independently Published
Release Date : 2025-02-11
Agentic Ai System Using Rag written by BRIAN. PITMAN and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Computers categories.
Agentic AI system using RAG: Enhancing Autonomous AI with Real-Time Knowledge using the Power of Retrieval-Augmented Generation is a comprehensive, hands-on guide designed for researchers, engineers, and entrepreneurs who are ready to harness the power of autonomous AI in the modern era. This book presents an in-depth exploration of Agentic AI in action, providing you with the tools to build self-directed AI systems that leverage advanced cognitive frameworks and cutting-edge retrieval-augmented generation (RAG) techniques. Throughout the book, you will discover how to construct robust Agentic AI architectures using Python, blending foundation models with practical guides to innovation in AI. The text covers everything from designing and implementing multi-agent systems with RAG to mastering the art of building production-ready, autonomous AI systems for real-world applications. With extensive code illustrations and step-by-step instructions, you will learn how to create intelligent systems that can dynamically update their knowledge base, ensuring real-time decision-making and adaptive responses. This book is your gateway to mastering agentic RAG architectures, whether you are interested in agentic AI in books, agentic AI architecture, or even applications that empower a generative AI wealth engine. You will gain insights into cognitive frameworks for agentic systems, practical approaches for iterative process optimization, and strategies for designing machine learning systems that are future-proof in an era of rapid technological change. In addition to technical details, the book also delves into how autonomous AI systems are revolutionizing industries such as finance, healthcare, and research. Learn how to build chatgpt millionaire making money online models and explore the transformative impact of intelligent systems for real-world applications. The guide provides a balanced perspective on both the theoretical foundations and practical challenges of deploying AI systems that operate autonomously, ensuring that you are well-equipped to implement and troubleshoot your own agentic AI projects. Whether you are seeking to develop a generative AI wealth engine, explore agentic AI systems using radio or rgb, or design sophisticated models for autonomous decision-making, this book offers a complete roadmap. It emphasizes best practices, iterative improvement, and the integration of reinforcement learning to enhance the adaptability of your AI applications. With a focus on scalability, performance, and ethical considerations, you will be empowered to contribute to the artificial intelligence revolution and future-proof your innovations. Step into the future of modern agentic artificial intelligence with this essential guide, and transform your approach to AI engineering. The knowledge and techniques presented in this book will enable you to build, customize, and deploy advanced RAG-powered AI agents that are ready to tackle the complexities of the digital age.
Agentic Ai System Leveraging Rag
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Author : JERRY. CANTER
language : en
Publisher: Independently Published
Release Date : 2025-03-27
Agentic Ai System Leveraging Rag written by JERRY. CANTER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-27 with Computers categories.
Book Description: Are you ready to harness the full potential of the next revolution in artificial intelligence? In Agentic AI System Leveraging RAG, you'll explore cutting-edge methods that blend the best of autonomous intelligence with real-time, context-aware insights through Retrieval-Augmented Generation (RAG). Designed as a practical, step-by-step guide, this groundbreaking resource unlocks the secrets of building intelligent, self-directed AI systems capable of dynamic adaptation and unparalleled accuracy. From strategic project planning to advanced cognitive frameworks, you'll gain authoritative knowledge on mastering AI agents and multi-agent systems enhanced by RAG. Each chapter combines deep technical insights with real-world applications and detailed case studies-empowering you to implement innovative, scalable solutions that meet the demands of modern industries. Inside, you'll discover how to: Develop robust cognitive architectures that enhance autonomous decision-making. Integrate RAG 2.0 to access real-time data streams, elevating your AI's predictive accuracy and responsiveness. Navigate ethical considerations, ensuring transparency and accountability in agentic AI systems. Scale your solutions efficiently, optimizing performance across diverse industries such as finance, healthcare, education, and environmental monitoring. Whether you're an AI practitioner, data scientist, business strategist, or innovator, this book provides the practical frameworks and visionary scenarios necessary to future-proof your AI solutions. With engaging explanations, personal insights from industry experiences, and actionable strategies, you'll confidently deploy intelligent systems for real-world applications, driving profound societal transformation. Embrace the AI revolution today. Transform your projects from concept to reality by mastering Agentic AI with RAG-and stay ahead in an era defined by autonomous intelligence. Perfect for fans of: Mastering Agentic RAG Autonomous AI Systems Building Self-Directed AI Systems Artificial Intelligence Revolution: Future-Proof Modern Agentic Artificial Intelligence Empower your innovations. Amplify your intelligence. Your journey into agentic AI starts here.
Building Agentic Ai System With Rag 2 0
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Author : Theo Marris
language : en
Publisher: Independently Published
Release Date : 2025-06-24
Building Agentic Ai System With Rag 2 0 written by Theo Marris and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
Agentic AI is transforming how intelligent systems operate-moving beyond static responses to dynamic, tool-using, goal-driven behavior. At the heart of this evolution is Retrieval-Augmented Generation 2.0 (RAG 2.0), a new architectural pattern that fuses long-term memory, contextual reasoning, multi-agent coordination, and modular tool use for building advanced AI systems that act, learn, and adapt over time. This book delivers a practical blueprint for applying RAG 2.0 to real-world agentic workflows across enterprise, healthcare, education, and automation sectors. Written by a seasoned AI practitioner and technical author specializing in LLM architectures, this guide is grounded in the latest research, including SafeRAG best practices, LangChain, LlamaIndex, Pinecone integration patterns, DSPy, GraphRAG, and AGI-aware agent design. Every chapter reflects current industry trends, community-driven implementations, and field-tested methodologies that have emerged from the leading AI labs and open-source communities. "Building Agentic AI System with RAG 2.0" is your complete roadmap to designing, implementing, and deploying powerful, scalable, and intelligent agents using the next generation of Retrieval-Augmented Generation techniques. Covering everything from system pipelines and memory management to prompt chaining, multi-agent orchestration, hallucination control, and ethical deployment, this book equips developers, architects, and AI enthusiasts with actionable insights and full-stack expertise. Whether you are building AI copilots, enterprise search assistants, autonomous agents, or educational tutors, this guide will accelerate your journey from experimentation to production readiness. Explore cutting-edge topics including vector databases and hybrid retrieval strategies, adaptive memory structuring, multi-modal extensions (GraphRAG & VideoRAG), safe deployment architectures, long-term personalization techniques, and cost-effective optimization. Detailed case studies demonstrate agentic AI in action across finance, clinical decision support, education, and more. Practical node-based examples using LangChain, LlamaIndex, and DSPy are provided throughout-designed to ensure hands-on application. This book is written for AI developers, data scientists, software engineers, ML ops practitioners, and anyone building advanced AI systems with LLMs. Whether you're transitioning from basic LLM use to advanced agent orchestration, or leading technical teams in deploying autonomous reasoning frameworks, you'll find clear guidance, practical architecture blueprints, and real-world use cases to elevate your skills. Stop building fragile prototypes and start engineering future-proof, scalable AI systems. The RAG 2.0 framework enables long-term performance, lower hallucination risk, and flexible integration across tools and memory-so your applications remain relevant, reliable, and continually evolving with new data and user feedback. This book is built for today's LLM stack and tomorrow's intelligent agents. Unlock the full potential of AI agents today. Buy "Building Agentic AI System with RAG 2.0" now and take the next step toward mastering Retrieval-Augmented Generation, declarative agent design, and production-grade agentic architecture. Start building intelligent, scalable systems that reason, remember, and act-on your terms.
Agentic Ai With Rag In Action
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Author : Ronald Taylor
language : en
Publisher: Independently Published
Release Date : 2025-02-16
Agentic Ai With Rag In Action written by Ronald Taylor and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-16 with Computers categories.
Agentic AI with RAG in Action: Enhance AI Agents, AI Prompt Engineering, Generative AI and AI Edge Sales Strategies Systems Using Agentic RAG Step into the future of artificial intelligence with this groundbreaking guide that transforms how you build, scale, and deploy autonomous AI systems. This book is your comprehensive, step-by-step roadmap to mastering Agentic AI using Retrieval-Augmented Generation (RAG), a powerful approach that fuses dynamic data retrieval with cutting-edge generative models. Whether you're an AI engineer, entrepreneur, or business leader, you'll discover practical strategies for designing production-ready systems that drive innovation and unlock real-world value. Learn how to create self-directed, intelligent agents using Python and cognitive frameworks that revolutionize sales, customer engagement, and business decision-making. Explore advanced topics like multi-agent systems with RAG, agentic AI architecture, and iterative processes for building modern, future-proof AI solutions. With detailed case studies, code illustrations, and a focus on ethical, scalable design, this book equips you to develop AI systems that are not only intelligent but also agile enough to adapt to ever-changing digital landscapes. From practical guides to innovation in generative AI wealth engines to designing machine learning systems with foundation models, "Agentic AI with RAG in Action" covers everything you need to build self-directed AI systems that excel in real-world applications. Harness the power of autonomous AI to drive profitability and stay ahead in a competitive market-whether you're making money online with ChatGPT millionaire strategies or deploying intelligent systems for enterprise-scale transformation. Take your AI expertise to the next level with a practical guide that blends technical mastery with strategic insights. This is the definitive resource for anyone determined to lead the artificial intelligence revolution and create innovative, intelligent systems that transform industries.
Building Agentic Ai With Rust
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Author : Evan Sterling
language : en
Publisher: Independently Published
Release Date : 2025-06-10
Building Agentic Ai With Rust written by Evan Sterling and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-10 with Computers categories.
Agentic AI-autonomous systems capable of perception, reasoning, and action-is redefining how we build intelligent applications. From AI customer service agents and healthcare assistants to real-time financial analysis tools, these systems integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and goal-oriented control. Rust, with its unmatched performance, safety guarantees, and asynchronous power via Tokio, is the ideal language to build scalable, high-concurrency AI agents that are production-ready. Written by a seasoned systems engineer and AI practitioner, Building Agentic AI with Rust is the first comprehensive guide focused on using Rust to build high-performance, autonomous AI agents. With deep real-world experience, clean architectural patterns, and a practical teaching style, this book bridges the gap between cutting-edge AI research and robust, deployable software engineering practices. This hands-on guide shows developers how to architect, implement, and deploy agentic AI systems using Rust and modern AI tools like OpenAI, Hugging Face, and vector search engines. Each chapter provides a step-by-step approach, from designing the agent loop to implementing a scalable RAG system and deploying with Docker and cloud services. You'll learn best practices for async programming with Tokio, profiling for performance, and implementing real-world use cases across industries. Implementing the Perceive-Reason-Act loop in Rust Architecting modular AI agents with traits and async tasks Integrating OpenAI and Hugging Face LLMs using structured prompts Building Retrieval-Augmented Generation (RAG) pipelines Scaling with Tokio, caching, and vector stores like Qdrant Packaging, containerizing, and deploying agents to AWS and GCP Monitoring, logging, and optimizing agents for production Full case studies: customer support, healthcare, and financial AI This book is written for Rust developers, AI engineers, system architects, and technical enthusiasts looking to build powerful autonomous agents with real-world capabilities. If you're comfortable with Rust and want to extend your skills into modern AI systems, this guide is for you. No prior experience with LLMs or RAG is required-concepts are introduced clearly and practically. Agentic AI is no longer experimental-it's production-ready, and it's here now. As the field of generative AI evolves rapidly, learning to build scalable, secure, and performant agents with Rust puts you ahead of the curve. Don't wait to catch up with the future-become one of the first engineers building it. Master the intersection of systems programming and generative AI. Build fast, safe, and intelligent autonomous agents with Rust today. Get your copy of Building Agentic AI with Rust and start coding the future of AI, now.
Agentic Rag
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Author : CAMILA. JONES
language : en
Publisher: Independently Published
Release Date : 2025-02-11
Agentic Rag written by CAMILA. JONES and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-11 with Computers categories.
Agentic RAG: Architecting Autonomous AI Systems with Retrieval-Augmented Generation is your definitive guide to the next frontier in artificial intelligence-a realm where autonomous decision-making meets cutting-edge retrieval-augmented generation technology. This book is meticulously designed for researchers, engineers, and advanced practitioners eager to harness the power of hybrid AI systems that can dynamically retrieve relevant information, generate insightful responses, and make intelligent decisions in real time. What You'll Discover: Foundational Concepts: Delve into the evolution of AI, core machine learning principles, and the transformative potential of retrieval-augmented generation. Understand how traditional and neural methods converge to create systems that are both robust and adaptable. Modular System Architectures: Learn how to design and integrate the key components of an Agentic RAG system-retrieval modules, generative engines, and decision-making frameworks. Detailed tutorials and step-by-step code walkthroughs empower you to build your own high-performance AI system from the ground up. Practical Implementation: Benefit from exhaustive hands-on examples that guide you through data collection, preprocessing, indexing, and system optimization. Discover best practices for deploying AI in real-world scenarios with case studies spanning healthcare diagnostics, financial risk management, customer interaction, robotics, IoT, and autonomous vehicles. Evaluation and Benchmarking: Master experimental design and benchmarking techniques. Learn how to evaluate your AI system's performance using industry-standard metrics such as BLEU, ROUGE, perplexity, and human evaluation-ensuring your solution is both accurate and efficient. Ethical, Legal, and Societal Considerations: Navigate the complex ethical, legal, and societal implications of deploying autonomous AI systems. Gain insights into data governance, regulatory compliance, and responsible AI practices that are essential for building trustworthy and sustainable technology. Future Directions: Explore emerging trends and next-generation decision-making algorithms that will shape the future of Agentic AI systems. From adaptive learning and meta reinforcement learning to multi-modal data integration, this book provides a visionary outlook on where AI is headed-and how you can be at the forefront of innovation. Whether you are looking to deepen your technical expertise, implement advanced AI solutions, or simply stay ahead of the curve in a rapidly evolving field, Agentic RAG offers the comprehensive knowledge and practical tools you need. Embrace the fusion of retrieval, generation, and autonomous decision-making to revolutionize how intelligent systems are built and deployed. Take your place in the future of AI-discover the transformative potential of Agentic RAG systems today!
Generative Ai With Langchain
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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-23
Generative Ai With Langchain written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
Agentic Rag With Crew Ai In Action
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Author : Devin Albert
language : en
Publisher: Independently Published
Release Date : 2025-07-10
Agentic Rag With Crew Ai In Action written by Devin Albert and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-10 with Computers categories.
Agentic RAG with Crew AI in Action: Build Smarter AI Systems with Agentic Reasoning, Task Chaining, and Dynamic Knowledge Access Modern AI isn't just about generating answers-it's about building systems that can think, retrieve, and act autonomously. Agentic RAG with Crew AI in Action shows you exactly how to combine Retrieval-Augmented Generation (RAG) with Crew AI to create intelligent, modular, and scalable multi-agent systems that reason through problems, retrieve knowledge when needed, and chain tasks together toward a goal. This book takes you inside the architecture of smart agentic systems, guiding you step-by-step as you design agents with roles, memory, planning capabilities, and tool use. You'll understand how to build agents that know when to query a vector database, when to rely on memory, how to use APIs or file systems, and how to collaborate across multiple agents with distinct responsibilities. Built for developers, ML practitioners, and AI system architects, the book offers everything from foundational concepts to advanced implementation techniques. Every chapter includes practical explanations, real-world code examples, and complete workflows-no fluff, no hand-waving. You'll learn how to: Use Crew AI to define roles, assign tasks, and coordinate agent behavior Integrate RAG pipelines using LangChain, LlamaIndex, and vector databases Structure multi-agent workflows with memory, feedback loops, and adaptive planning Build agents that retrieve data, use tools, reflect on output, and make decisions Handle logging, debugging, security, and scaling across distributed environments You'll also explore powerful use cases like automated research assistants, legal brief generators, and business data analyzers-real projects that showcase the full potential of agentic systems in action. This book isn't just theory-it's a full engineering guide for the next generation of AI. If you're ready to stop building static chatbots and start building AI systems that can think through tasks, work with knowledge, and operate autonomously across complex workflows, this is your playbook. Build smarter. Build faster. Build agentic AI that works. Get started today.
Agentic Ai Systems
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Author : Roberto Pizzlo
language : en
Publisher: Independently Published
Release Date : 2025-06-15
Agentic Ai Systems written by Roberto Pizzlo and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-15 with Computers categories.
Agentic AI Systems: Build Multi-Agent Workflows with LangChain, MCP, RAG & Ollama (A Practical Guide to Local LLM Orchestration, Retrieval-Augmented Generation, and Autonomous Agents) Unlock the power of local LLMs, agentic AI architectures, and multi-agent orchestration with this hands-on guide designed for developers, AI engineers, and system architects building intelligent applications beyond the cloud. In an era where data privacy, autonomous workflows, and cost-effective deployments are critical, this book offers a production-ready blueprint using LangChain, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and Ollama. Whether you're designing AI copilots, deploying autonomous agents, or developing secure on-premise AI systems, this guide helps you go from concept to execution with confidence. What You'll Learn: Set up a complete agentic AI stack with LangChain, LangGraph, MCP, and Ollama Run private LLMs like Llama 3 and Mixtral with full control using Ollama Fine-tune models with LoRA/QLoRA for domain-specific applications Design and orchestrate multi-agent systems using LangGraph and graph-based coordination Build robust Retrieval-Augmented Generation pipelines using FAISS and Chroma Implement secure message-passing and streaming using MCP Handle authentication, observability, and compliance (GDPR, HIPAA, SOC 2) Deploy agents with Docker, Kubernetes, and scalable CI/CD pipelines Who This Book Is For: AI engineers and backend developers working with LLMs and LangChain Security-conscious teams needing private and auditable AI workflows DevOps and MLOps professionals deploying containerized AI systems Researchers and tech leads building autonomous agent systems Anyone interested in real-world agentic AI with local deployment capabilities Unlike cloud-reliant AI books or overly academic texts, Agentic AI Systems delivers actionable blueprints for building and deploying real systems on local infrastructure. You'll explore hands-on code, architecture diagrams, and reusable patterns that scale from laptops to clusters. No fluff-just proven strategies and reproducible workflows grounded in current LLM capabilities. Roberto Pizzlo is an AI infrastructure engineer and systems architect specializing in agentic orchestration and secure LLM deployments. Known for translating cutting-edge AI concepts into practical engineering, he brings a wealth of expertise in LangChain, LangGraph, RAG architectures, and edge AI systems. His experience bridges research, enterprise, and open-source ecosystems-making this book an essential guide for professionals navigating the fast-evolving world of autonomous AI. This guide reflects 2025 technologies and best practices, including the latest versions of LangChain, Ollama (v0.2.16+), CUDA 12.9, and RAG toolchains. It ensures your understanding remains relevant in a rapidly changing AI landscap
Agentic Rag System With Mcp And Langchain
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Author : Rowan Creed
language : en
Publisher: Independently Published
Release Date : 2025-06-28
Agentic Rag System With Mcp And Langchain written by Rowan Creed and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-28 with Computers categories.
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 patterns: multi-hop reasoning, critic agents, query refinement Vector store tuning, reranking strategies, latency mitigation, and tool drift handling Production-ready orchestration: 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. Accelerate your AI roadmap without starting from scratch. Unlock the future of AI automation. Grab your copy of Agentic RAG System with MCP and LangChain today and start building advanced LLM-powered agents that reason, remember, and act with purpose. Whether you're launching next-gen AI products or optimizing internal enterprise systems, this book is your blueprint for building trustworthy, modular, and production-grade AI agents.