Here, we describe the agentic environment that made those results possible in the first place. More importantly, we explain why this environment is now central to how we evaluate agents, reduce variance in online benchmarks, and generate labelled trajectory data that can be reused to train smaller, cheaper computer‑use agents.
In this blog, we trace the evolution of browser agents, track the benchmarks that are available currently, and talk about some of the core optimizations that we’ve been able to deliver for our partners, making their production-grade browser-use agents faster and more accurate.
A modular architecture for building flexible, on-prem voice agents using LiveKit and Palantir’s OSDK.
A real-time architecture that connects enterprise data and context to frontline workers through voice-to-voice AI.
