{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/ee0455604e524656a37ee641e77d19c2\" frameborder=\"0\" width=\"1670\" height=\"1252\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1252,"width":1670,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1252,"thumbnail_width":1670,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/ee0455604e524656a37ee641e77d19c2-b181794b419a61ad.gif","duration":80.59,"title":"Optimizing AI Agent Performance and Troubleshooting Challenges 🚀","description":"In this video, I discuss the challenges I've faced in software engineering over the past 10 years, particularly in optimizing workflows with the introduction of AI agents. I highlight issues like retry storms and contact thrash, which can complicate performance. To address these problems, I've utilized tools like Llama Index and Redis for indexing, as well as Horizon 3 for security scans. I emphasize the importance of understanding these issues to improve our systems. I encourage viewers to consider how we can better monitor and enhance our AI agents' performance."}