{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/4bd0d5522db74170882f7e402032cba6\" frameborder=\"0\" width=\"1668\" height=\"1251\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1251,"width":1668,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1251,"thumbnail_width":1668,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/4bd0d5522db74170882f7e402032cba6-4f29fe2a0a958508.gif","duration":95.659,"title":"Breaking AI Customer Service Containment Ceilings","description":"This Loom explains how to break the “static containment ceiling” that prevents AI customer service agents from resolving customer conversations without escalation. It describes a three-loop recursive engine that uses live predictions to anticipate customer frustration, autonomous soft pruning to remove response paths that lead to escalation, and behavioral dreaming to cluster failure patterns and deploy automated playbook patches. The speaker notes that when agents fail to contain a frustrated parent trying to order a daughter’s birthday gift for tomorrow, the conversation is escalated and the valuable transcript is buried in a database. The goal is to avoid repeat mistakes by using continuous resolution loops and continuously improving the agent based on daily customer data."}