A control layer cuts autonomous agents’ token use by 36.4%
A new architecture helps long-running language-model agents stop when the work is done, cutting mean token use 36.4% without reducing benchmark success. Global Executive Control separates action generation from project-level decisions about scope, evidence, resources, and stopping. In 24,000 matched simulations, it reached 96.57% hard-goal success versus 96.53% for a multiple-candidate control, while eliminating measured pre-completion drift; live-model validation is still needed.