Managing one's digital footprint is overwhelming, as it spans multiple platforms and involves countless context-dependent decisions. Recent advances in agentic AI offer ways forward by enabling holistic, contextual privacy-enhancing solutions. Building on this potential, we adopted a ''human-as-the-unit'' perspective and investigated users' cross-context privacy challenges through 12 semi-structured interviews. Results reveal that people rely on ad hoc manual strategies while lacking comprehensive privacy controls, highlighting nine privacy-management challenges across applications, temporal contexts, and relationships. To explore solutions, we generated nine AI agent concepts and evaluated them via a speed-dating survey with 116 US participants. The three highest-ranked concepts were all post-sharing management tools with half or full agent autonomy, with users expressing greater trust in AI accuracy than in their own efforts. Our findings highlight a promising design space where users see AI agents bridging the fragments in privacy management, particularly through automated, comprehensive post-sharing remediation of users' digital footprints.

翻译：管理个人数字足迹是一项艰巨任务，因其涉及跨平台操作与无数依赖情境的决策。近期智能体人工智能的进展为实现整体化、情境感知的隐私增强解决方案提供了可能。基于此潜力，我们采用"以人为单位"的视角，通过12场半结构化访谈探究用户跨情境隐私挑战。研究发现：人们依赖临时手动策略而缺乏全面的隐私控制，揭示了跨应用程序、时间情境和人际关系的九大隐私管理挑战。为探索解决方案，我们构建了九类AI智能体概念原型，并通过116名美国参与者的快速约会式调查进行评估。三个评分最高的概念均为具备半自主或全自主能力的分享后管理工具，用户对AI准确性的信任度超过对自身努力的信任。我们的研究揭示了一个充满前景的设计空间：用户期待AI智能体能够弥合隐私管理中的碎片化问题，特别是通过自动化、全面的分享后数字足迹修复机制来实现。