CONTEXT ENGINEERING FOR MULTI-AGENT SYSTEMS : move beyond prompting to build a context engine, a... transparent architecture of context and reasoning.

Format:

Book

Author/Creator:

Rothman, Denis.

Language:

English

Subjects (All):

Systems engineering.

Multiagent systems.

Physical Description:

1 online resource

Place of Publication:

[S.l.] : PACKT PUBLISHING LIMITED, 2025.

Summary:

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.

Contents:

Intro

Context Engineering for Multi-Agent Systems

Move beyond prompting to build a Context Engine, a transparent architecture of context and reasoning

Contributors

About the author

About the reviewers

Table of Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

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How to Unlock

1

From Prompts to Context: Building the Semantic Blueprint

Understanding context engineering

Level 1: The basic prompt (zero context)

Level 2: The better context (linear context)

Level 3: The good context (goal-oriented context)

Level 4: The advanced context (role-based context)

Level 5: The semantic blueprint

SRL: from linear sequences to semantic structures

Building an SRL notebook in Python

The main function: visualize_srl

Defining the semantic roles

The plotting engine: _plot_stemma and canvas setup

Dynamic positioning and drawing the stemma (graph)

Running SRL examples

Example 1: Business pitch

Example 2: Technical update

Example 3: Project milestone

Engineering a meeting analysis use case

Layer 1: Establishing the scope (the "what")

Layer 2: Conducting the investigation (the "how")

Layer 3: Determining the action (the "what next")

Summary

Questions

References

Further reading

Get This Book's PDF Version and Exclusive Extras

2

Building a Multi-Agent System with MCP

Architecting the MAS workflow with MCP

Building an MAS with MCP

Initializing the client

Defining the protocol

Message format

Transport layers

Protocol management

Building the agents

Creating the helper function

Defining the Researcher agent

Defining the Writer agent

Building the Orchestrator

Running the system

Error handling and validation

Building robust components for the LLM

Validating MCP messages

Adding agent specialization controls and validation

The final Orchestrator with a validation loop

Running the final robust system

The evolution of AI architecture

Tools for building agent systems

Further reading

Subscribe for a free eBook

3

Building the Context-Aware Multi-Agent System

Architecting a dual RAG MAS

Phase 1: Data preparation

Phase 2: Runtime execution analysis

RAG pipeline data ingestion (context and knowledge)

Installation and setup

Initializing the Pinecone index

Data preparation: the context library (procedural RAG)

Data preparation: the knowledge base (factual RAG)

Helper functions for chunking and embedding

Process and upload (upsert) data

Context library

Knowledge base

Building the context-aware system

Defining the agents

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781806690053

OCLC:

1552039529