video1.0<iframe src="https://www.loom.com/embed/5e733a54748e4f82809014e4f38a7b93" frameborder="0" width="2560" height="1920" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>19202560Loomhttps://www.loom.com19202560https://cdn.loom.com/sessions/thumbnails/5e733a54748e4f82809014e4f38a7b93-6ee2225f126dae34.gif88.821354This Loom explains how Dispatch IQ uses a multi-agent system to re-plan a service company’s day when disruptions occur. It describes a plumbing scenario where a sick technician and an emergency require rearranging crews while customers wait, with AI agents generating re-planning logic that a dispatcher reviews and approves. In each round, six agents propose plans in parallel, a plane-code scoring process evaluates simulated bad days, and a critic agent turns results into lessons, with five of six champions influenced by the critics. In a 50-day locked test, the team improved outcomes versus a baseline by 38 versus 12 for random search, raising on-time jobs from 59% to 74% and reducing mis-emergency promises from 13 to 4.