<?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/63500eaef6574a8fa4fb40ddc6d78f0b&quot; frameborder=&quot;0&quot; width=&quot;2228&quot; height=&quot;1671&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1671</height><width>2228</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1671</thumbnail_height><thumbnail_width>2228</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/63500eaef6574a8fa4fb40ddc6d78f0b-4ed2ae643e4b9930.gif</thumbnail_url><duration>268.312</duration><title>Optimizing Agent Workflows for Efficient Data Scraping 🚀</title><description>In this video, I addressed the challenges you&apos;re facing with the agents logging into paid websites and the limitations you&apos;re encountering due to the number of steps involved. I explained that we have guardrails in place to prevent agents from running indefinitely, which is likely causing the issues you&apos;re seeing. I suggested a workflow where Agent 1 conducts research and then triggers Agent 2 to scrape a second website, allowing for more efficient data handling. Additionally, I mentioned the possibility of using agent delegation features to streamline communication between agents. Please consider implementing these suggestions to enhance your workflow.</description></oembed>