A traditional semantic layer in the context of BI is great for specific metric definitions (like revenue, churn, ARPU). However, they are usually hand constructed by data teams using very specific syntax through a dedicated layer like LookML and are connected directly to a BI tool like Looker.
这一观察揭示了传统语义层的局限性：它们虽然解决了特定指标定义问题，但过于手工化、工具绑定，难以适应现代AI代理的动态需求。这暗示了语义层需要进化以支持更广泛的AI应用场景。
A modern data context layer should essentially become a superset of what a semantic layer would traditionally cover. Sure, specific metric definitions can be hard-coded, but a modern context layer should include more to ensure agent autonomy – canonical entities, identity resolution, specific instructions to dissect tribal knowledge, proper governance guidance, and more.
这段文字提出了一个令人深思的观点，即现代数据上下文层应超越传统语义层的限制。它不仅包括硬编码的指标定义，还应包括规范实体、身份解析、部落知识解析和治理指导等，以确保AI代理的自主性和准确性。
A traditional semantic layer in the context of BI is great for specific metric definitions (like revenue, churn, ARPU). However, they are usually hand constructed by data teams using very specific syntax through a dedicated layer like LookML and are connected directly to a BI tool like Looker.
令人惊讶的是：商业智能(BI)中的传统语义层虽然对特定指标定义很有用，但通常是由数据团队手动构建的，使用特定的语法如LookML，并直接连接到BI工具如Looker。这种手动构建方式与现代AI系统所需的自动化和灵活性形成鲜明对比，揭示了传统数据工具与现代AI需求之间的根本冲突。