AI Agents 的能力取决于其对数据的理解。CARTO 为 AI Agents 引入语义模型，提供其他层无法具备的空间上下文。
AI Agents are only as good as their understanding of your data. CARTO brings semantic models to AI Agents, with spatial context no other layer provides.
我们认为这是值得支持的正确举措。统一标准意味着，您的团队在数据仓库中集中维护的指标定义，可无缝导入 CARTO，并与整个分析技术栈保持一致。CARTO 是该倡议的工作组成员，专注于将地理空间能力纳入规范之中。我们正与社区协作，推动几何类型、空间索引及空间关系成为所有平台与工具（而不仅限于 CARTO）所原生支持的一等语义信息。我们已发起公开讨论，就空间数据应如何描述（包括字段级空间数据与空间维度）提出建议；同时，我们自身的实现已采用 Ossie 的扩展机制，在标准逐步完善的过程中承载此类上下文信息。以下为该空间部分在实际应用中的示例：下方是 Ossie 语义模型中的两个字段，均使用了 CARTO 的扩展来传递空间语义。第一个字段为人口普查区块组（census block group）层级的多边形几何体：借助该上下文，AI 智能体即可明确：第一列是一个多边形，可用于绘制分级统计图（choropleth maps）以及在人口普查区块组层级执行空间连接；第二列为 H3 索引，分辨率为 8，可向上聚合至分辨率 7，并以 H3 图层形式渲染。将此逻辑扩展至模型中的全部数据集后，AI 智能体便不再对您的空间数据进行猜测，而是基于明确定义的语义开展推理。语义模型将 AI 智能体从仅能回答有关您数据的通用问题，转变为能够按照您业务自身定义的方式（含空间数据）作答。这是迈向 GIS 团队与业务用户均可信赖的智能体的重要一步。语义模型支持现已在 CARTO AI Agents 中正式上线。欢迎开启为期 14 天的免费试用，或预约演示，亲身体验其在您自有数据上的实际效果。 哈维尔（Javier）现任 CARTO 首席产品官（Chief Product Officer），全面负责 CARTO 的产品组合、客户体验及创新路线图。其职责覆盖完整的产品开发周期与客户体验管理。 公共部门机构已普遍部署 Oracle 的空间引擎。CARTO 将其转化为自助式 AI 智能体，使团队无需排队等待，即可即时获取答案。 企业团队为何从 Esri ArcGIS 迁移至云原生 CARTO，以及 AI 智能体如何端到端、分步骤完成迁移全过程。
We think this is the right effort to back. A shared standard means that metric definitions your team maintains centrally in your data warehouse can travel into CARTO and stay consistent with the rest of your analytics stack. CARTO is a Working Group Member of the initiative, focused on bringing geospatial into the specification. We are working with the community so that geometry types, spatial indexes, and spatial relationships become first-class semantic information across every platform and tool, not only ours. We have opened public discussions proposing how spatial data should be described (field-level spatial data and spatial dimensions), and our own implementation already uses Ossie’s extension mechanism to carry this context while the standard matures. Here is what the spatial part looks like in practice. Below are two fields from an Ossie semantic model, each using CARTO’s extension to carry the spatial meaning. The first is a polygon geometry at the census block group level: With this context, the agent knows the first column is a polygon it can use for choropleth maps and spatial joins at the census block group level, and that the second is an H3 index at resolution 8 that rolls up to resolution 7 and renders as an H3 layer. Multiply that across every dataset in the model and the agent stops guessing about your spatial data and starts reasoning with it. Semantic models turn an AI Agent from something that can answer questions about your data into something that answers them the way your business defines them, spatial data included. It is a step toward agents that GIS teams and business users can both trust. Semantic model support is available in CARTO AI Agents today. Start a free 14-day trial, or request a demo to see it working on your own data. Javier is CARTO's Chief Product Officer and is globally responsible for CARTO’s product portfolio, customer experience, and innovation roadmap. Javier's responsibilities span the complete product development and customer experience cycle. Public sector agencies already have Oracle's spatial engine. CARTO turns it into self-service AI Agents, giving teams instant answers without a queue. Why enterprise teams move from Esri ArcGIS to cloud-native CARTO, and how an AI agent handles the migration end to end, step by step.