Acquire2Act: Evaluating Information-to-Action Boundaries in LLM Agents

An agent that gathers information must decide when to stop gathering and start executing. Does its control policy adapt appropriately as evidence becomes available? We study this question with Acquire2Act, a controlled evaluation of the acquisition-to-execution boundary on 200 application, data-science, and software-engineering tasks. Acquire2Act varies source-grounded information while fixing execution infrastructure, exposing acquisition targets, verified facts, and advancement decisions. Eight controller configurations achieve 60.5–70.0% task success, versus 86.5% for a privileged information-need policy. Natural trajectories show strong responses to information progress, but reports of local target completion can coexist with premature advancement, and already avoidable acquisition targets can persist. In a matched restoration experiment on 130 tasks, supplying the complete construction-defined fact set increases advancement by 48.5 and 36.9 percentage points for two controllers. Nevertheless, they continue acquiring information on 40.8% and 34.6% of complete-information inputs. A separate ablation does not find a consistent positive effect of an explicit completion-status field. These results distinguish information responsiveness from appropriate boundary control: controllers use new evidence, yet neither observed progress nor restored facts ensure reliable acquisition exit under the fixed task and execution contract.