In this article, you will learn what context engineering is and how to apply it systematically to keep AI agents reliable, cost-efficient, and accurate in production. Topics we will cover include: How to treat the context window as a constrained resource and understand the financial and cognitive costs of token mismanagement. How to structure context layers — separating static from dynamic content, managing conversation history, and designing retrieval as a budget decision. How to evaluate and monitor context quality in production using probe-based evaluation and context-specific metrics. Effective Context Engineering for AI Agents: A Developer’s Guide Image by Author Introduction When AI agents break down in production, the problem is rarely the model. More often, the context window is mismanaged: bloated with stale history, redundant retrieval results, and raw tool outputs that bury the signal the model actually needs.…