ETF Agent details its Deep Research engineering and tests
ETF Agent developers say they implemented Deep Research inside the existing Native Research Loop rather than creating a separate agent, adding authorized resource tiers, runtime-aware delivery guidance, versioning and recovery. Paired Standard/Deep experiments showed that both modes could run research and deliver formal answers, but fewer calls did not consistently translate into better accuracy, speed or cost. The developers explicitly say this engineering implementation is not a feature of the current public Open ETF Agent, and the findings are vendor-reported.
The report provides a concrete example of adding a deeper research mode to an existing agent runtime while candidly documenting that additional resources alone did not guarantee better financial-research results.
Deep Research：从资源升级到研究交付，我们怎么实现，又测出了什么？
普通的投研问答可能只需核对一只基金的费率；另一类问题却要求穿透组合、读取跨期报告、处理修订版本、让不同研究分支协作，最后给出一份连贯的结论。这两类任务都叫 Research，但对资源和交付节奏的要求不一样。
在 ETF Agent 中，我们没有为后一类任务重新搭建一套 Agent。做法是让同一个 Native Research Loop获得经过授权的更高资源档位，再根据当前实际生效的研究模式调整交付指导：研究过程中持续反馈进度，已有独立依据的部分允许先交付，关键结论则更倾向于在完成证据比较后组织成文。
这篇文章先看业界如何处理长研究，再回答 ETF Agent 的三个问题：为什么原有 Research 不够；Deep 在代码与运行时究竟改变了什么；真实 Standard/Deep 配对运行交出了什么结果。这里所述的 Deep 实现依据 ETF Agent 工程仓，不把它冒称为当前公开版 Open ETF Agent 已经具备的功能。
#ETF Agent#Open ETF Agent#ETF Agent Deep Research#ETF Agent Native Research Loop