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，因其涉及多个平台且需要基于具体情境做出无数决策。近期代理式人工智能的进展为实现整体化、情境化的隐私增强解决方案提供了可能。基于这一潜力，我们采用"以人为单位"的视角，通过12次半结构化访谈研究了用户跨情境隐私挑战。研究发现：人们依赖临时性人工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，我们构建了九个AI代理概念，并通过116名美国参与者参与的快速约会式调查进行评估。排名前三的概念均为具备半自主或全自主能力的共享后管理工具，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前景广阔的设计空间：用户认为AI代理能够弥合隐私管理中的碎片化问题，特别是通过自动化、全面的共享后数字足迹修复机制来实现。