<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/7f9292044a774128958d6270834a791d&quot; frameborder=&quot;0&quot; width=&quot;1670&quot; height=&quot;1252&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1252</height><width>1670</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1252</thumbnail_height><thumbnail_width>1670</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/7f9292044a774128958d6270834a791d-88bab667ef4e3d54.gif</thumbnail_url><duration>60.333</duration><title>Building Waycool for Agentic Commerce Brand Data</title><description>This Loom demonstrates Waycool, a personal shopping assistant for Agentic Commerce, and how it creates structured brand data for conversational shopping. It explains that you can upload a PDP URL or use the browser extension to pull product catalog information from a site like Wayfair. The user then types their profile details, style, and total budget, optionally adds a file to narrow the request, and locks the profile. Clicking the button deploys multiple subconscious agents to review the refined data and identify matching products.</description></oembed>