How OpenAI and Anthropic each build data agents differently - DataChain
English summary
This article compares how OpenAI and Anthropic build data agents, highlighting that raw file access is insufficient without metadata, schemas, and lineage. OpenAI's internal system benefits from a structured warehouse environment, while Anthropic emphasizes context and tool use. The key takeaway is that a semantic layer is essential for agents to understand data meaning and relationships. The effectiveness of data agents depends heavily on the surrounding data infrastructure.
Chinese summary
本文比较了OpenAI和Anthropic构建数据代理的方式，指出仅有原始文件访问是不够的，还需要元数据、模式和血统。OpenAI的内部系统得益于结构化仓库环境，而Anthropic则强调上下文和工具使用。关键结论是，语义层对于代理理解数据含义和关系至关重要。数据代理的有效性在很大程度上取决于周围的数据基础设施。
Key points
Raw file access alone is not enough for data agents.
仅靠原始文件访问不足以支撑数据代理。
OpenAI's internal system works well due to a rich warehouse environment with strong structure and context.
OpenAI的内部系统由于具有强结构和上下文的丰富仓库环境而运行良好。
Anthropic focuses on context, tool use, and structured agent design.
Anthropic专注于上下文、工具使用和结构化代理设计。
The agent's effectiveness is limited by the underlying data infrastructure.
代理的有效性受底层数据基础设施的限制。
A semantic layer is needed to inform agents about data meaning, table relationships, and trustworthiness.
需要语义层来告知代理数据的含义、表关系以及可信度。