Summary

In this chapter, we introduced context engineering and explained why it is essential for building reliable and scalable AI agents. We examined how context differs from static prompts, where it comes from, and why unmanaged context leads to problems such as performance degradation, hallucinations, and inconsistent behavior. Using Claude Code as a practical example, we explored four core context engineering strategies: writing context through persistent memory, selecting relevant context dynamically, compressing context to keep it manageable, and isolating context using specialized sub-agents. We also discussed the role of system prompts, showing how effective prompts strike a balance between being too rigid and too vague. By now, you should have an idea of how modern AI agents manage context and how both developers and users can influence their behavior. In the next chapter, we will build on this foundation and begin working with more advanced agentic workflows, using the HookHub...