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Context Engineering for Multi-Agent Systems

Context Engineering for Multi-Agent Systems

By : Denis Rothman
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Context Engineering for Multi-Agent Systems

Context Engineering for Multi-Agent Systems

4 (1)
By: Denis Rothman

Overview of this book

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
Table of Contents (16 chapters)
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12
Other Books You May Enjoy
13
Index

Appendix A

Context Engine Reference Guide

The context engine is a sophisticated, multi-agent system designed to transform the interaction paradigm with LLMs. It moves beyond the limitations of simple prompt-response cycles, which often rely on statistical chance and produce unpredictable results. Instead, the Context Engine provides a structured environment for directed creation, enabling engineers to control not just what the AI generates, but how it reasons within defined boundaries. This approach shifts the user's role from that of a questioner to that of an architect, designing the very landscape of the AI's thought process.

This engine is the practical implementation of context engineering, the discipline of transforming generative AI into a fully controlled and reliable partner. It achieves this control through the strategic deployment of agents orchestrated by a central Planner and Executor, all communicating idx_a6aa8796via a standardized Model Context Protocol (MCP...

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