<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Knowledge Graphs on Uni Matrix Zero</title><link>https://unimatrixz.com/tags/knowledge-graphs/</link><description>Recent content in Knowledge Graphs on Uni Matrix Zero</description><generator>Hugo</generator><language>en</language><copyright>Stephan Froede</copyright><lastBuildDate>Wed, 12 Aug 2026 22:12:36 +0200</lastBuildDate><atom:link href="https://unimatrixz.com/tags/knowledge-graphs/index.xml" rel="self" type="application/rss+xml"/><item><title>Generative AI Production Architecture</title><link>https://unimatrixz.com/topics/ai-production-architecture/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/topics/ai-production-architecture/</guid><description>&lt;p&gt;A generative AI production architecture defines more than which model receives a prompt. It determines where truth lives, how decisions become actions, which intermediate states remain inspectable and what must happen before a generated result becomes part of the system.&lt;/p&gt;</description></item><item><title>Graph RAG vs. Agent Memory vs. World State</title><link>https://unimatrixz.com/topics/ai-production-architecture/graph-rag-agent-memory-world-state/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/topics/ai-production-architecture/graph-rag-agent-memory-world-state/</guid><description>&lt;p&gt;Graph RAG, agent memory and world state are often grouped under “giving the model more context.” That description is too broad for architecture. They solve different problems and carry different levels of authority.&lt;/p&gt;</description></item><item><title>Knowledge Graphs as the Control Plane for AI Agents</title><link>https://unimatrixz.com/topics/ai-production-architecture/knowledge-graphs-as-agent-control-plane/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/topics/ai-production-architecture/knowledge-graphs-as-agent-control-plane/</guid><description>&lt;p&gt;An AI agent needs more than retrieved paragraphs. It needs an inspectable map of the entities it may act on, how they relate, which sources support a claim and which transitions are permitted. A knowledge graph can provide that semantic control plane.&lt;/p&gt;</description></item><item><title>World Model AI vs. Studio Architecture</title><link>https://unimatrixz.com/topics/ai-production-architecture/world-model-ai-vs-studio-architecture/</link><pubDate>Wed, 12 Aug 2026 00:00:00 +0000</pubDate><guid>https://unimatrixz.com/topics/ai-production-architecture/world-model-ai-vs-studio-architecture/</guid><description>&lt;p&gt;World model AI can learn how a scene may evolve, predict the consequences of candidate actions and provide useful priors about physical reality. That is a major capability. It is not, however, the same as owning an explicit production world whose entities, history, geometry, permissions and accepted state can be queried and controlled.&lt;/p&gt;</description></item></channel></rss>