video1.0<iframe src="https://www.loom.com/embed/e39e9bdd44d44955a4882a05cc1a82e5" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/e39e9bdd44d44955a4882a05cc1a82e5-8cc151cdc9629c8f.gif300.98This Loom explains how to use AI agents to create hyper-personalized, dynamic landing pages for different customer types because both AI and humans otherwise see the same static site. The speaker cites Cloudflare data that 57% of requests are made by AI and describes an auto workflow that runs guided analysis, using Co-Frame with an 82% human-friendly gap and 70% agent execution performance. The process generates personas across enterprise and startups, runs 20 tasks per persona, and produces detailed audit findings with an action plan, with planned site modifications. A key risk discussed is an audit job creation bug lacking authentication, rate limits, concurrency caps, and per-client quotas, with Bright Data API key exposure costing $10 to $25 per run and leading to potential losses of $250.