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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

Summary

This chapter introduced context engineering as an emerging skill for turning LLMs into reliable, goal-oriented systems. Instead of relying on unstructured prompts, we showed how control comes from engineering the informational environment, culminating in the semantic blueprint as the most precise form of direction.

We traced this shift through a five-level progression: from zero-context prompts that yielded generic outputs to linear, goal-oriented, and role-based contexts, each proving that structured input drives better results. To formalize this approach, we introduced SRL, a method that breaks sentences into predicate, agent, patient, and modifiers, supported by a Python visualizer that renders these roles as a stemma diagram.

Finally, we applied these skills in a meeting analysis use case, where context chaining turned a raw transcript into actionable outcomes. Step by step, the process reduced noise, highlighted new developments, surfaced implicit dynamics, and produced structured summaries and follow-up actions.

Together, SRL and context chaining provide both the theoretical framework and the practical workflow, respectively, to move beyond prompting. We are now ready to engineer agentic contexts in the next chapter.

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