AI 中文总结
针对编码智能体无法明确项目关键信息的问题，提出MAGE框架及理论，通过外化表征与分配权威构建可信自主系统，经案例与工业案例验证，可将商用智能转化为持久工程进展。
AI 中文摘要
编码智能体提升了实现能力，但不会自动明确项目意图、系统结构或验收证据。当实现相对于工程判断变得充足时，稀缺工作转向选择有用的抽象、生成证据以及确定哪些义务支配验收。现有工作流通过更大的提示词、仓库检索或逐变更审查解决了部分缺口，但仍需要智能体和工程师重构关键属性。作为替代方案，我们提出了基于模型的智能体软件工程（MAGE）。MAGE是一种从商用智能构建可信自主系统的框架和理论，它解决了表征问题和权威问题：它外化了回答工程问题所需的最小有目的表征，然后通过约束、传感器、验证器和网关赋予确定的义务相称的权威。它保留了不确定的意图，将反复的重构和判断转化为可被后续工作继承的持久工程结构。我们从一个纵向案例开发了MAGE，并通过6个独立报告的工业案例对其进行了完善。在这些来源中，MAGE解释了外化知识、受限行动、独立评估和保留的人类权威如何构成受管控的工程环境，并提出了测试该环境何时将商用智能转化为持久工程进展的方法。
英文摘要
Coding agents increase implementation capacity without automatically making project intent, system structure, or acceptance evidence explicit. As implementation becomes abundant relative to engineering judgment, the scarce work shifts toward choosing useful abstractions, producing evidence, and determining which obligations govern acceptance. Existing workflows address parts of this gap through larger prompts, repository retrieval, or perchange review, but still require agents and engineers to reconstruct consequential properties. As an alternative, we present Model-Based Agentic Software Engineering (MAGE). MAGE is a framework and a theory for building trustworthy autonomy from commodity intelligence. MAGE addresses a representation problem and an authority problem: it externalizes the smallest purposeful representation needed to answer an engineering question, then gives settled obligations proportionate authority through constraints, sensors, validators, and gates. It keeps uncertain intent open and turns recurring reconstruction and judgment into durable engineering structure that later work can inherit. We developed MAGE from a longitudinal case and refined it through six independently reported industrial accounts. Across these sources, MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
