Last year, reasoning models unlocked the ability to post-train agents for long horizon tasks. Being introduced to coding agents was quite a magical experience for me! I also thought that the unlock would lead to models being deployed for all kinds of other tasks. However, the broader deployment of AI seems to be hindered by the gap between the environments curated for RL training and the complex orchestration logic that is actually used in practice. Further progress requires creating increasingly complex synthetic environments and leveraging new techniques to improve agents based on their experiences in production. I joined Applied Compute to work on bridging that gap between theory and practice. Models can and should be useful for any task today; we just need to integrate the model with the infrastructure it needs for its tasks. We've translated our early training reps with customers into a platform built for agents that deliver outcomes. Really excited by the progress and the path forward to broader deployment. There's still a lot more work to be done!