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.

翻译：AI增强生态系统（多个AI组件通过共享数据与基础设施相互交互的互联系统）正成为智慧城市、自主车队和智能平台的架构常态。然而，从业者依赖的架构文档框架——arc42和C4模型——专为确定性软件设计，无法捕捉概率性行为、数据依赖演化或机器学习/软件双重生命周期。这一缺口带来监管后果：欧盟AI法案（第2024/1689号法规）通过附件四规定技术文档要求，但现有框架均未提供结构化支持，且高风险系统的执法将于2026年8月2日生效。我们提出RAD-AI——一种向后兼容的扩展框架，通过八个AI专用部分增强arc42，并以三种图表扩展增强C4，辅以系统的欧盟AI法案附件四合规映射。与六位经验丰富的软件架构从业者进行的监管覆盖评估初步证据表明，RAD-AI将附件四的可处理性从约36%提升至93%（平均评分），并展现出对现有框架的显著改进。对两个生产级AI平台（Uber Michelangelo、Netflix Metaflow）的比较分析捕捉到标准框架遗漏的八个额外AI专用关注点，并证明文档缺陷是结构性问题而非领域特定。一项说明性智能出行生态系统案例研究揭示了标准符号下不可见的生态系统级关注点，包括级联漂移和差异化合规义务。