本文由白鲸开源CEO郭炜投递并参与由数智猿×数据猿×上海大数据联盟共同推出的《2025中国数智化转型升级先锋人物》榜单/奖项评选。 在经历了半个世纪的数据仓库演进后，企业的数智化转型正迎来一次真正意义上的范式重构。从Inmon提出的"主题域"到Kimball的"雪花模型"，从 Teradata的MPP引擎到Snowflake的云原生架构，所有的数据技术栈都有一个共同点：它们都是以"人"为最终消费者而设计的。报表、仪表盘、SQL查询， 这些工具的逻辑都是帮助人类决策。 然而，随着AI Agent时代的到来，这一逻辑正在被彻底颠覆。Agent不再是被动的工具，而是能够感知环境、理解业务语义、主动执行任务的"数字员 工"。当数据的最终消费者不再是分析师和管理者，而是Agent，本质上意味着企业数智化的用户边界被打破。传统的"人驱动决策支持系统"（DSS）模式 正在让位于"Agent驱动的智能交互"。 在过往，企业往往陷入数据建设的形式主义：无止境的数据建模、报表开发和工具堆叠，形成了巨大的内耗。而当Agent成为主角后，数智化转型的重心 将从"拼工具、卷报表"回归到"拼业务、拼价值"。这不仅是技术趋势的演进，更是企 ...

Core Viewpoint - The article discusses the transformative impact of Agentic AI on the software ecosystem, particularly how traditional data warehouses are being challenged by new architectures that prioritize semantic and responsive data handling over structured querying [1][3][34]. Group 1: Industry Changes - Snowflake's recent CEO change signals a paradigm shift in the data warehouse landscape, moving from a focus on traditional data warehousing to an AI-first approach [2][3]. - The emergence of Agentic AI, which acts as an intelligent agent capable of understanding and executing tasks, raises questions about the relevance of traditional decision support systems designed for human users [4][5][22]. - The traditional data warehouse, once a critical asset for enterprises, may become merely a repository of raw data for these intelligent agents, diminishing its value [6][30]. Group 2: Evolution of Data Architecture - The evolution of data warehouse architecture has seen significant milestones, from Bill Inmon's foundational concepts in the 1970s to the rise of cloud-native solutions like Snowflake in 2015 [9][18]. - The article outlines how the introduction of big data technologies and cloud computing has reshaped the data landscape, leading to a decline in the dominance of traditional MPP architectures [16][17]. - The concept of Agentic Data Stack is introduced as a new architecture that integrates data and semantics, designed to meet the needs of AI agents [36][39]. Group 3: Future Implications - The future of data warehouses will likely involve a shift from human-centric designs to architectures that cater to AI agents, fundamentally altering how data is stored, processed, and utilized [30][31]. - The article predicts that as Agentic AI becomes more prevalent, the roles of various business functions will be redefined, with agents taking over tasks traditionally performed by humans [25][27]. - The transition to Agentic Data Stack is expected to reduce the construction cycle of data warehouses significantly, enabling real-time data access and processing capabilities [39][40].