<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Abstract Meaning Representation on Uni Matrix Zero</title><link>https://unimatrixz.com/tags/abstract-meaning-representation/</link><description>Recent content in Abstract Meaning Representation on Uni Matrix Zero</description><generator>Hugo</generator><language>en</language><copyright>Stephan Froede</copyright><lastBuildDate>Wed, 12 Aug 2026 14:37:12 +0200</lastBuildDate><atom:link href="https://unimatrixz.com/tags/abstract-meaning-representation/index.xml" rel="self" type="application/rss+xml"/><item><title>From AMR to Execution Graphs for Agentic Workflows</title><link>https://unimatrixz.com/topics/ai-production-architecture/amr-execution-graphs-agentic-workflows/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/topics/ai-production-architecture/amr-execution-graphs-agentic-workflows/</guid><description>&lt;p&gt;Agent frameworks increasingly expose workflows as graphs. Nodes call models or tools, edges control routing, and persisted state makes long-running work resumable. At first glance, Abstract Meaning Representation seems made for this moment: it also turns language into a graph.&lt;/p&gt;
&lt;p&gt;But the two graphs answer different questions.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;AMR describes meaning. An execution graph prescribes computation.&lt;/strong&gt; Connecting them requires a compiler boundary, not a shared diagram format.&lt;/p&gt;</description></item></channel></rss>