Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation. However, existing agents typically suffer from a misalignment between their tool-integrated reasoning trajectories and recommendation feedback, limiting their ability to distinguish fine-grained user preferences. To address these challenges, we propose AgenticRec, an agentic recommendation framework that formulates recommendation as a tool-integrated reasoning process over a recommendation-oriented tool suite. Built upon this framework, we further develop a dedicated two-stage training paradigm tailored for recommender agents. In the first stage, we introduce Recommendation-Oriented Trajectory Activation, optimize the agentic recommendation ability under implicit feedback. In the second stage, Progressive Preference Refinement further refines the agent through bidirectional preference reasoning over self-bootstrapped hard pairs, progressively sharpening preference boundaries. Theoretical analysis and extensive experiments demonstrate the effectiveness of AgenticRec. Our code is available at https://anonymous.4open.science/r/AgenticRec-FB16.

翻译：基于大语言模型的推荐智能体为个性化推荐提供了有前景的范式。然而，现有智能体通常面临工具集成推理轨迹与推荐反馈之间的不匹配问题，限制了其区分细粒度用户偏好的能力。为解决这些挑战，我们提出AgenticRec——一种将推荐建模为面向推荐工具套件上的工具集成推理过程的自适应推荐框架。在此基础上，我们进一步开发了专为推荐智能体设计的双阶段训练范式。第一阶段引入面向推荐的轨迹激活，在隐式反馈下优化自适应推荐能力；第二阶段通过渐进式偏好细化，利用自引导硬样本对进行双向偏好推理，逐步锐化偏好边界。理论分析与大量实验证明了AgenticRec的有效性。我们的代码发布于https://anonymous.4open.science/r/AgenticRec-FB16。