为AI生态设计可合规的架构文档框架，解决现有工具无法覆盖欧盟AI法案要求的问题。
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
- 在arc42和C4模型基础上新增11个AI专用模块，支持概率行为与数据演化描述
- 使欧盟AI法案附件IV合规覆盖率从36%提升至93%，实证显著改进
- 适合智能城市、自动驾驶等高风险AI系统的架构师与合规团队使用
AI增强型生态系统（由多个通过共享数据与基础设施交互的AI组件构成）正成为智慧城市、自动驾驶车队及智能平台的主流架构范式。然而，当前从业者依赖的arc42与C4架构文档框架专为确定性软件设计，难以捕捉概率行为、数据驱动演化或机器学习/软件双生命周期特性。这一差距带来监管风险：欧盟AI法案（2024/1689号条例）第附件IV要求提供技术文档，但现有框架无结构化支持，高风险系统强制执行始于2026年8月2日。本文提出RAD-AI，作为向后兼容的扩展框架，为arc42新增八个AI专用章节，为C4添加三个图表扩展，并配套欧盟AI法案附件IV的系统化合规映射。六位资深架构师的监管覆盖率评估显示，RAD-AI将附件IV可覆盖度从约36%提升至93%（均值），显著优于现有框架。对两个生产级AI平台（Uber Michelangelo、Netflix Metaflow）的对比分析揭示了标准框架遗漏的八项关键问题，证明缺陷源于结构性而非领域特异性。一个智慧出行生态案例研究进一步暴露了系统级挑战，包括级联漂移与差异化合规义务，这些在传统表示法下完全不可见。
原文摘要 · Abstract (English)
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.
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