Interface design can be automated and improved through agent experience—evolving interfaces from task trajectories is a practical way to boost agent performance that's complementary to model scaling.
This paper introduces Agent S4, a system that automatically designs and improves computer interfaces for AI agents by learning from task experience. Rather than relying on hand-built interfaces, a meta-agent analyzes how task agents perform on training tasks and evolves better interfaces and usage strategies.