video1.0<iframe src="https://www.loom.com/embed/74a3b28067e24c1b886054ba90a90aa5" frameborder="0" width="1664" height="1248" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12481664Loomhttps://www.loom.com12481664https://cdn.loom.com/sessions/thumbnails/74a3b28067e24c1b886054ba90a90aa5-5442ba3ff082a739.gif142.2511In this video, I introduce one of our flight booking simulators where an AI agent is tasked with booking flights from San Francisco to New York for a weekend in September. The simulator mimics real-world environments closely, allowing agents to learn and generalize their skills effectively. We’ve implemented verifiers to check the accuracy of tasks against ground truth data, which serves as a reward signal for reinforcement learning. I'm excited about the growing demand for simulators as we start to integrate multiple environments for multi-application tasks. I encourage you to explore these capabilities and think about how we can leverage them in our projects.