{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/b45444f85a3241128d685d0eaeb59379\" frameborder=\"0\" width=\"1838\" height=\"1378\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1378,"width":1838,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1378,"thumbnail_width":1838,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/b45444f85a3241128d685d0eaeb59379-ff9c05970e048668.gif","duration":693.49,"title":"Achieving Autonomous Agents through Context Graphs and Compounding Intelligence","description":"In this demo, I showcased how we can operationalize context graphs to create truly autonomous agents that learn and improve over time. Unlike traditional AI systems that start from scratch with each deployment, our architecture enables compounding intelligence, allowing the system to accumulate knowledge and enhance decision-making capabilities. We implemented evaluation gates to ensure governed autonomy, and I highlighted our unique feature, Triggered Evolution, which boosts confidence levels in decision-making. I also introduced prompt-based analytics to address dashboard sprawl, making it easier for executives to access critical data like MTTR. I encourage you to explore these features and consider how they can enhance our operational efficiency."}