Mastering Agentic RAG (Paperback)

Lingua: inglese

Editore: Independently Published, 2025

9798269013589

Da: CitiRetail, Stevenage, Regno UnitoCitiRetail

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Venditore AbeBooks dal 29 giugno 2022

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Paperback. Engineer the Future of Intelligent Systems with Agentic AIIf you've ever hit the ceiling of what a single RAG model can do, this book will change how you think about AI forever.Mastering Agentic RAG is a complete developer's playbook for building intelligent, production-ready Agentic AI systems, networks of specialized agents that work together to solve complex, real-world problems that ordinary models can't handle.Written for developers, AI engineers, and system architects, this book demystifies the architecture behind multi-agent orchestration, showing you how to transform Large Language Models (LLMs) into structured, reliable systems. You'll learn to move beyond simple prompts and build Agentic AI ecosystems, modular, autonomous, and engineered for collaboration and scale.What This Book OffersEnd-to-End Framework for Agentic AI: Learn the Model-Context-Protocol (MCP) architecture, the blueprint for building scalable Agentic AI systems that coordinate multiple agents seamlessly.Hands-On Multi-Agent Project: Build a complete Multi-Agent Financial Analyst Bot using Python, FastAPI, LangChain, and Docker. Watch it perform deep analysis, web research, synthesis, and reporting, just like a digital analyst team.Production-Ready Design: Learn to deploy your Agentic AI system using Docker Compose, manage your secrets securely, and integrate CI/CD pipelines with GitHub Actions.Performance, Reliability, and Scale: Evaluate your multi-agent system with custom metrics, structured logging, and human-in-the-loop feedback. Discover how to make your Agentic AI systems resilient, observable, and cost-efficient.Why This Book Stands OutUnlike books that only teach prompting or basic RAG pipelines, Mastering Agentic RAG gives you the full architectural playbook for building intelligent systems that think and act collectively. It's not about "using AI", it's about engineering intelligence.You'll discover how to design a society of specialized agents, a Researcher, Analyst, Verifier, and Orchestrator, all communicating through a shared context and coordinated by an MCP server. This is Agentic AI in action: modular, explainable, and infinitely scalable.Each concept is backed by real code, real workflows, and a real project that bridges the gap between AI research and production software development.Table of Contents (Highlights)Introduction: Why single agents fail and Agentic AI winsChapter 1: The Architect's Dilemma - the limits of a lone agentChapter 2: The MCP Blueprint - foundations of Agentic AIChapters 3-5: Building specialized agents and the MCP serverChapters 6-9: Orchestrating, testing, containerizing, and deployingChapters 10-12: Evaluation, monitoring, scaling, and future directionsWho This Book Is ForThis book is for AI engineers, Python developers, system designers, and data scientists who are ready to go beyond chatbots.If you can code and understand APIs, Mastering Agentic RAG will teach you how to design, deploy, and scale agentic AI systems that act intelligently, communicate clearly, and evolve autonomously. Master the principles of Agentic AI, learn to design real-world multi-agent architectures, and lead the next wave of innovation.Don't wait for the future to arrive build it. Buy this book today and start mastering Agentic RAG This item is printed on deman Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9798269013589

Titolo
Mastering Agentic RAG (Paperback)
Autore
Lucas Grant
Editore
Independently Published
Anno di pubblicazione
2025
Condizione
new
Rilegatura
Paperback
Lingua
inglese
ISBN 13
9798269013589

CitiRetail

Stevenage, Regno Unito

Venditore con 5 stelle

Venditore AbeBooks dal 29 giugno 2022

Tariffe di spedizione da Regno Unito a U.S.A.

ArticoloDa 7 a 14 giorni lavorativiDa 7 a 60 giorni lavorativi
Primo articoloEUR 43,05EUR 43,05
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