Neuware - Build AI that thinks in context using semantic blueprints, multi-agent orchestration, memory, RAG pipelines, and safeguards to create your own Context EngineFree 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 safeguardsBook 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.\*Email sign-up and proof of purchase requiredWhat 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 productionWho 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 The Semantic Blueprint: From Prompt to Context 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.

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Bibliographic Details

Title: Context Engineering for Multi-Agent Systems : Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning
Publisher: Packt Publishing Nov 2025
Publication Date: 2025
Language: English
ISBN 10: 1806690055
ISBN 13: 9781806690053
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 191 millimeters height by 22 millimeters depth
Weight: 737 grams

About this title

Synopsis

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.

*Email sign-up and proof of purchase required

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.

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

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.

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