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Context Engineering

This chapter introduces context engineering and explains why it has become a critical concept when building and using modern AI agents. While many AI systems are often described as "just prompts wrapped around an LLM," this chapter shows why that view breaks down as agents become more complex, long-running, and tool-driven. You will learn where context comes from, why it keeps growing, and how poor context handling leads to degraded performance, higher costs, hallucinations, and inconsistent behavior.

The goal of this chapter is to give you a clear mental model for context engineering and show how it is applied in practice. We start by explaining how context engineering evolved from prompt engineering, then use Claude Code as a concrete example of how modern agents write, select, compress, and isolate context. Towards the end, we will examine system prompts, why they still matter, and how to design them effectively.

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