Thanks - trajectory export was key for us since most teams want both eval and training data.

On non-determinism: we actually handle this in two ways. For our simulated environments (HTML/JS apps like the Slack/CRM clones), we control the full render state so there's no variance from animations or loading states. For native OS environments, we use explicit state verification before scoring - the reward function waits for expected elements rather than racing against UI timing. Still not perfect, but it filters out most flaky failures.

Windows Arena specifically - we're focusing on common productivity flows (file management, browser tasks, Office workflows) rather than the edge cases you mentioned. UAC prompts and driver dialogs are exactly the hard mode scenarios that break most agents today. We're not claiming to solve those yet, but that's part of why we're open-sourcing this - want to build out more adversarial tasks with the community.