<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Graphviz-Dot on Corey Daley</title><link>https://coreydaley.dev/tags/graphviz-dot/</link><description>Recent content in Graphviz-Dot on Corey Daley</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 06 Mar 2026 12:00:00 -0500</lastBuildDate><atom:link href="https://coreydaley.dev/tags/graphviz-dot/rss.xml" rel="self" type="application/rss+xml"/><item><title>Attractor: When Chaos Theory Meets AI Pipeline Orchestration</title><link>https://coreydaley.dev/posts/2026/03/attractor-dot-pipeline-orchestration/</link><pubDate>Fri, 06 Mar 2026 12:00:00 -0500</pubDate><guid>https://coreydaley.dev/posts/2026/03/attractor-dot-pipeline-orchestration/</guid><description>&lt;p&gt;Most AI automation breaks for the same reason: the workflow is hidden inside scripts, prompts, and tribal knowledge. Attractor takes a different path — one borrowed from chaos theory. In dynamical systems, an attractor is a state a system naturally converges toward even through turbulence.&lt;/p&gt;
&lt;p&gt;Apply that to AI workflows, and you get directed graphs that pull multi-LLM execution through branching, retries, human review gates, and failure recovery toward a defined goal. The entire codebase is AI-generated, which makes it a working proof-of-concept of the Software Factory philosophy.&lt;/p&gt;
&lt;p&gt;If we can declaratively orchestrate AI agents today, what should we still insist on owning as humans?&lt;/p&gt;
&lt;p&gt;Read more at &lt;a
 href="https://coreydaley.dev/posts/2026/03/attractor-dot-pipeline-orchestration/" target="_blank" rel="noopener noreferrer"&gt;https://coreydaley.dev/posts/2026/03/attractor-dot-pipeline-orchestration/&lt;/a&gt;
&lt;/p&gt;</description></item></channel></rss>