AI agents rely on repeated decisions to accomplish tasks, typically using large language models (LLMs) as core decision makers. The newly introduced Jev can generate decisions faster and at lower cost, promising workflow efficiency gains. We evaluated five representative agents in two settings—embodied tasks in ALFWorld and mobile app interactions in AndroidWorld—across 4,500 task executions, measuring task effectiveness, decision‑level and task‑level resource use, and execution behavior. Results show that Jev consistently reduces decision cost and latency for all agents, yet task‑level benefits vary with the workflow. Success rates stay near baseline in ALFWorld but drop noticeably for two mobile agents. Extra reasoning steps and interactions absorb part of the decision savings, while failed recoveries and premature completions limit successful delivery. Although spending per attempt declines, the average cost per successful outcome rises for some agents. The findings highlight that integrating efficient decision models requires preserving progress, supporting recovery, and verifying completion to translate local savings into real task value.
Review: The work cautions that faster decisions alone do not guarantee better overall performance; system‑level recovery and verification are equally essential.