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Book Overview & Buying
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Table Of Contents
Context Engineering for Multi-Agent Systems
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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.