{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/4a99528a1875476690551bcd75ec036c\" frameborder=\"0\" width=\"1728\" height=\"1296\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1296,"width":1728,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1296,"thumbnail_width":1728,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/4a99528a1875476690551bcd75ec036c-a909f61664d7d35d.gif","duration":179.8959,"title":"Optimizing AI Agents with Percival 🌟","description":"In this video, I demonstrate how to use Patronus Percival to automatically fix bugs in AI agents. I walk through a simple example where our agent retrieves weather information but initially includes irrelevant details. By using Percival's prompt suggestions, we can optimize the agent to provide only the necessary information. I encourage you to try implementing these prompt fixes in your own projects for better results."}