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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

7

High-Fidelity RAG and Defense: The NASA-Inspired Research Assistant

In the previous chapters, we successfully engineered a robust and efficient context engine. We have built a system capable of reasoning, planning complex workflows, and managing its own operational costs. Now, we confront the next critical stage in elevating our system to a truly enterprise-grade asset: ensuring its outputs are not just plausible, but also trustworthy. In a professional context, an answer without evidence is merely an opinion. In real-world projects, we cannot afford to produce opinions. This chapter is dedicated to transforming our engine from a powerful tool into a reliable and secure partner by tackling the twin pillars of trust: verifiability and security.

To ground these advanced concepts in a practical application, we will build a NASA-inspired research assistant. This use case embodies the highest standards of intellectual rigor, where every claim must be traceable to its source. We will upgrade...

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