Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

翻译：工程系统设计——无论是机电、控制还是嵌入式系统——通常以临时方式进行，需求隐含而不明确，从设计意图到参数的追溯性基本缺失。现有的规约驱动和系统化设计方法主要针对软件领域，而AI辅助工具往往在解决方案生成阶段介入，而非问题构建阶段。物理系统设计中的人机协作仍鲜有探索。本文提出Design-OS，一种轻量级、规约驱动的工程系统设计工作流，包含五个阶段：概念定义、文献调研、概念设计、需求定义和设计定义。规约作为人类设计师与AI智能体之间的共享契约；每个阶段生成结构化工件，保持可追溯性并支持智能体增强执行。我们将Design-OS置于需求驱动设计、系统化设计框架和AI辅助设计管线的背景下进行定位，并通过一个控制系统设计案例——基于两种旋转倒立摆平台（开源的SimpleFOC反作用轮和商业化的Quanser Furuta摆）——演示了同一规约驱动工作流如何适应根本不同的实现方式。空白模板及完整设计案例工件已共享于公开仓库中，以支持可复现性和复用。该工作流使设计过程可见且可审计，并将AI的规约驱动编排从软件扩展到物理工程系统设计。