Rethinking Environments for Long-Horizon Work — Rayan Garg, Theta Software

Long-horizon AI agents require a nuanced definition of time horizons that moves beyond simple token counts or human-centric benchmarks. Effective evaluation of these agents necessitates assessing environment complexity, specifically regarding sequential tool coordination and state changes, rather than just parallelizable tasks. Because deterministic verifiers often fail in complex, open-ended domains, "judge" models are essential for assessing trajectories and ensuring reward signals remain accurate. Current industry benchmarks for long-horizon tasks, such as GDPVal and ApexAgents, often suffer from saturation, narrow scope, and a lack of granular reward signals, failing to capture true model performance. Robust evaluation frameworks must prioritize detailed rubrics and queryable trajectories to accurately measure agent capabilities in high-stakes environments like finance, where tasks involve significant ambiguity and require sophisticated, non-linear problem-solving skills.