Agent evaluation benchmarks are hindered by excessive environment interaction overhead and poorly balanced task difficulty, rendering aggregate scores unreliable. To mitigate these issues, researchers have introduced ACE-Bench, a novel benchmarking framework centered on a unified grid-based planning task. This task requires agents to fill hidden slots in a partially completed schedule, allowing for scalable horizons and controllable difficulty under lightweight environments. By streamlining environment interactions, ACE-Bench reduces overhead, with environment interaction accounting for up to 41% of total evaluation time in existing benchmarks1. This development enables more efficient and accurate assessment of agent performance. The introduction of ACE-Bench has significant implications for practitioners, as it provides a more reliable and efficient means of evaluating agent capabilities, allowing for more informed decision-making in the development and deployment of artificial intelligence systems.