How to build reliable AI workflows with agentic primitives and context engineering

Developers often start their AI exploration using simple prompts with tools like GitHub Copilot, but as tasks become more complex, a structured approach is necessary. This guide introduces a three-part framework for AI-native development, emphasizing agentic primitives, which are reusable building blocks, and context engineering, ensuring AI agents focus on relevant information. The framework consists of Markdown prompt engineering, agent primitives, and context engineering, enabling reliable and consistent AI workflows. GitHub Copilot CLI facilitates running, debugging, and automating these workflows locally, enhancing connectivity with repositories and issues. By leveraging agent primitives and context engineering, developers can create systematic AI development processes, turning ad-hoc experimentation into repeatable and scalable agentic workflows. The framework also discusses the importance of runtime management and package distribution for scaling and sharing agent primitives, with tools like APM providing the necessary infrastructure for managing these processes efficiently.