While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This paradigm ignores a critical opportunity: the idle time between interactions is largely wasted, leaving agents unable to prepare for future user needs. To bridge this gap, we introduce ProAct, a proactive agent architecture that leverages idle-time compute to anticipate and fulfill likely upcoming user needs. By analyzing evolving dialogue history together with persistent memory, ProAct predicts upcoming needs and iteratively acquires information, allowing the agent to resolve knowledge gaps and prepare evidence before the user initiates a query.To rigorously evaluate proactive capabilities, we also introduce ProActEval, a comprehensive benchmark comprising 200 scenarios across 40 domains, featuring predictable need chains and diverse user cognitive profiles. Empirical results demonstrate significant advantages over reactive baselines. ProAct accelerates task completion by reducing required turns by 14.8%, decreases user effort by 11.7%, and cuts hallucination rates by 28.1% on ProActEval. Furthermore, MemBench evaluations confirm that ProAct achieves state-of-the-art reflective accuracy, underscoring its sustained and robust performance.

翻译：尽管AI Agent在推理和工具使用方面展现出卓越能力，但其本质仍是反应式的：仅在收到用户明确提示后才会进行计算响应。这一范式忽视了关键机遇：交互间的空闲时间被大量浪费，导致Agent无法为未来用户需求做好准备。为填补这一空白，我们提出ProAct——一种主动式Agent架构，通过利用空闲计算资源来预判并满足可能出现的用户需求。通过分析动态演进的对话历史与持久化记忆，ProAct能预测即将到来的需求并迭代式获取信息，使Agent能在用户发起查询前消除知识盲区并准备证据。为严格评估主动能力，我们同时推出ProActEval——一个涵盖40个领域200个场景的综合基准测试，其中包含可预测的需求链与多样化的用户认知特征。实证结果表明，相比反应式基线方法，ProAct展现出显著优势：在ProActEval上，任务完成所需交互轮次减少14.8%，用户努力降低11.7%，幻觉率下降28.1%。此外，MemBench评估证实ProAct达到了最先进的反思准确率，凸显其持续且稳健的性能表现。