Prompt engineering and context engineering are two complementary methods used to optimize the performance of large language models (LLMs), with prompt engineering focusing on instructing AI agents on specific actions and context engineering providing the necessary data for these actions. While prompt engineering is ideal for creating deterministic AI agents that offer reliable outcomes, context engineering provides depth by integrating historical data, memory, and service relationships, allowing AI to perform complex tasks similar to human employees. These approaches are not mutually exclusive but rather enhance each other, as context engineering can automate data access, reducing the need for frequent prompt adjustments. An example of their synergy is seen in Port's platform, where AI agents leverage both methods to autonomously resolve incidents or identify when human intervention is required. Together, these strategies can improve workflow agility and scalability, transforming the role of developers into writing prompts that guide AI in code creation.