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Context Engineering for Multi-Agent Systems

Context Engineering for Multi-Agent Systems

By : Denis Rothman
4 (1)
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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

6

Building the Summarizer Agent for Context Reduction

This chapter marks an important transition, tackling one of the core business challenges in enterprise AI: managing the financial and technical costs of large contexts. We will confront this challenge through a new idx_4ac90476capability, context reduction, by introducing a specialist idx_34e17afeagent called the Summarizer. Its purpose is to intelligently reduce content volumes, acting as a gatekeeper against practical constraints such as token limits and API costs.

Before writing a single line of code, we will revisit the system’s architectural blueprint and conceptually design a new capability: proactive context management. We will visualize how the Summarizer integrates into the existing framework. With a clear plan idx_1329a398established, we will move to implementation, translating the design into a self-contained agent and integrating it into the engine’s discoverable toolkit. By the end of this chapter, you will...

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