While prompt engineering has been widely discussed, context engineering is becoming essential for building dependable AI agents. This article examines how context is structured, maintained, and dynamically updated to improve model performance. It highlights key mechanisms, differences from prompt-based approaches, and real-world use cases where context engineering enhances accuracy and reduces errors.