<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>System Architecture on DataAgentX</title><link>https://e343c64b.dataagentx.pages.dev/en/tags/system-architecture/</link><description>Recent content in System Architecture on DataAgentX</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Mon, 07 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://e343c64b.dataagentx.pages.dev/en/tags/system-architecture/index.xml" rel="self" type="application/rss+xml"/><item><title>A Seven-Layer Architecture for Production Data Agents</title><link>https://e343c64b.dataagentx.pages.dev/en/tech/20260907-architecture/</link><pubDate>Mon, 07 Sep 2026 00:00:00 +0000</pubDate><guid>https://e343c64b.dataagentx.pages.dev/en/tech/20260907-architecture/</guid><description>&lt;p&gt;“Why did new-customer conversion fall this month?” sounds like a SQL question. It is not.&lt;/p&gt;&#10;&lt;p&gt;Before touching a warehouse, a useful Data Agent has to establish what &lt;em&gt;new customer&lt;/em&gt; means, which conversion window applies, whether the metric changed recently, which data the requester is allowed to see, and what evidence would make an explanation credible. A query can run perfectly and still answer the wrong question.&lt;/p&gt;&#10;&lt;p&gt;Production Data Agents therefore need more than a language model and a database connector. They need a chain of responsibilities that turns ambiguous intent into a result another person can inspect. The seven-layer model below is a way to design that chain—and, when it breaks, to locate the failure.&lt;/p&gt;</description></item></channel></rss>