Although AI agents have demonstrated impressive capabilities in long-horizon reasoning, their reliability is severely hampered by the ``Spiral of Hallucination,'' where early epistemic errors propagate irreversibly. Existing methods face a dilemma: uncertainty quantification (UQ) methods typically act as passive sensors, only diagnosing risks without addressing them, while self-reflection mechanisms suffer from continuous or aimless corrections. To bridge this gap, we propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals. Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary. This enables the agent to balance efficient execution and deep deliberation dynamically. Extensive experiments on closed-loop benchmarks and open-ended deep research tasks demonstrate that our training-free approach achieves superior performance and trajectory-level calibration. We believe this principled framework AUQ represents a significant step towards reliable agents.

翻译：尽管AI智能体在长程推理中展现出令人印象深刻的能力，但其可靠性受到"幻觉螺旋"的严重制约——早期认知误差会不可逆地传播。现有方法面临两难困境：不确定性量化方法通常作为被动传感器，仅诊断风险而不予解决；而自我反思机制则易陷入持续或盲目的修正循环。为弥合这一鸿沟，我们提出统一的**双过程智能体不确定性量化框架**，将言语化不确定性转化为主动的双向控制信号。该架构包含两个互补机制：**系统一**通过不确定性感知记忆模块，隐式传播言语化置信度与语义解释以规避盲目决策；**系统二**则通过不确定性感知反思模块，将这些解释作为理性线索，仅在必要时触发定向推理时解析。这使得智能体能够动态平衡高效执行与深度思辨。在闭环基准测试和开放式深度研究任务上的大量实验表明，我们这种无需训练的方法实现了卓越的性能与轨迹级校准。我们相信这一原则性框架AUQ标志着向可靠智能体迈出了重要一步。