video1.0<iframe src="https://www.loom.com/embed/cb404a9d925046508916a9cf0bd8bd7a" frameborder="0" width="1664" height="1248" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12481664Loomhttps://www.loom.com12481664https://cdn.loom.com/sessions/thumbnails/cb404a9d925046508916a9cf0bd8bd7a-28cb1a3c1e89e78a.gif271.3819In this video, I demonstrate the functionality of my final Python script, which utilizes a parallel research agent architecture to assess compliance for an auto loan approval system under EU regulations. We ran five sequential and parallel subagents, achieving a risk classification of high with a score of 65 out of 100 and a confidence level of 95%. The relevant articles identified were six, eight, and nine, along with documented compliance gaps. I also showcased a visual representation of the results and ran additional scenarios, including prohibited and minimal risk systems. Please take a look at the results and let me know if you have any questions or need further clarification.