Context Engineering for Multi-Agent Systems
Language: English
Published by Packt Publishing, 2025
- Softcover
- New
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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Seller Inventory # DB-9781806690053
- Title
- Context Engineering for Multi-Agent Systems
- Author
- Denis Rothman
- Publisher
- Packt Publishing
- Publication year
- 2025
- Condition
- New
- Binding
- PAP
- Language
- English
- ISBN 10
- 1806690055
- ISBN 13
- 9781806690053
- Item weight
- 818 grams
Build AI that thinks in context using semantic blueprints, multi-agent orchestration, memory, RAG pipelines, and safeguards to create your own Context Engine
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
- Design semantic blueprints to give AI structured, goal-driven contextual awareness
- Orchestrate multi-agent workflows with MCP for adaptable, context-rich reasoning
- Engineer a glass-box Context Engine with high-fidelity RAG, trust, and safeguards
Book Description
Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios.
Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence.
By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence.
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What you will learn
- Develop memory models to retain short-term and cross-session context
- Craft semantic blueprints and drive multi-agent orchestration with MCP
- Implement high-fidelity RAG pipelines with verifiable citations
- Apply safeguards against prompt injection and data poisoning
- Enforce moderation and policy-driven control in AI workflows
- Repurpose the Context Engine across legal, marketing, and beyond
- Deploy a scalable, observable Context Engine in production
Who this book is for
This book is for AI engineers, software developers, system architects, and data scientists who want to move beyond ad hoc prompting and learn how to design structured, transparent, and context-aware AI systems. It will also appeal to ML engineers and solutions architects with basic familiarity with LLMs who are eager to understand how to orchestrate agents, integrate memory and retrieval, and enforce safeguards.
Table of Contents
- From Prompts to Context: Building the Semantic Blueprint
- Building a Multi-Agent System with MCP
- Building the Context-Aware Multi-Agent System
- Assembling the Context Engine
- Hardening the Context Engine
- Building the Summarizer Agent for Context Reduction
- High-Fidelity RAG and Defense: The NASA-Inspired Research Assistant
- Architecting for Reality: Moderation, Latency, and Policy-Driven AI
- Architecting for Brand and Agility: The Strategic Marketing Engine
- The Blueprint for Production-Ready AI
"Synopsis" may belong to another edition of this title.
About the Author
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
"About the title" may belong to another edition of this title.
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