RAD-AI：重新思考面向AI增强生态系统架构文档
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
AI总结：
本文提出RAD-AI框架，通过扩展arc42和C4模型，解决传统架构文档无法捕捉AI概率行为和数据依赖演化的问题，提升欧盟AI法案合规性。
摘要：
AI增强生态系统（相互连接的系统，其中多个AI组件通过共享数据和基础设施交互）正成为智能城市、自动驾驶车队和智能平台的架构常态。然而，架构文档框架arc42和C4模型是为确定性软件设计的，无法捕捉概率行为、数据依赖的演变或双ML/软件生命周期。这一差距具有监管影响：欧盟AI法案（第2024/1689号条例）要求通过附录IV的技术文档，而现有框架无法提供结构化支持，自2026年8月起对高风险系统进行强制执行。本文提出RAD-AI，一个向后兼容的扩展框架，通过增加八个AI特定部分到arc42和三个图扩展到C4，辅以系统性的欧盟AI法案附录IV合规映射。与六名经验丰富的软件架构师的监管覆盖评估表明，RAD-AI将附录IV的可访问性从约36%提升到93%（均值评分），并显示出显著优于现有框架的改进。对两个生产AI平台（Uber Michelangelo、Netflix Metaflow）的比较分析捕捉到八个额外的AI特定关切，证明文档缺陷是结构性而非领域特定的。一个示例智能移动生态系统案例研究揭示了生态系统层面的关切，包括级联漂移和差异化合规义务，这些在标准记号下是不可见的。
英文摘要：
AI-augmented ecosystems (interconnected systems where multiple AI components interact through shared data and infrastructure) are becoming the architectural norm for smart cities, autonomous fleets, and intelligent platforms. Yet the architecture documentation frameworks practitioners rely on, arc42 and the C4 model, were designed for deterministic software and cannot capture probabilistic behavior, data-dependent evolution, or dual ML/software lifecycles. This gap carries regulatory consequence: the EU AI Act (Regulation 2024/1689) mandates technical documentation through Annex IV that no existing framework provides structured support for, with enforcement for high-risk systems beginning August 2, 2026. We present RAD-AI, a backward-compatible extension framework that augments arc42 with eight AI-specific sections and C4 with three diagram extensions, complemented by a systematic EU AI Act Annex IV compliance mapping. A regulatory coverage assessment with six experienced software-architecture practitioners provides preliminary evidence that RAD-AI increases Annex IV addressability from approximately 36% to 93% (mean rating) and demonstrates substantial improvement over existing frameworks. Comparative analysis on two production AI platforms (Uber Michelangelo, Netflix Metaflow) captures eight additional AI-specific concerns missed by standard frameworks and demonstrates that documentation deficiencies are structural rather than domain-specific. An illustrative smart mobility ecosystem case study reveals ecosystem-level concerns, including cascading drift and differentiated compliance obligations, that are invisible under standard notation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
