Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behavior change, and patient support, most current implementations fall short of facilitating user intent and fostering accountability. This contrasts with prior work on supporting longitudinal needs, where follow-up, coherent reasoning, and sustained alignment with individuals' goals are critical for both effectiveness and safety. In this paper, we draw on established clinical and personal health informatics frameworks to define what it would mean to orchestrate longitudinal health interactions with AI agents. We propose a multi-layer framework and corresponding agent architecture that operationalizes adaptation, coherence, continuity, and agency across repeated interactions. Through representative use cases, we demonstrate how longitudinal agents can maintain meaningful engagement, adapt to evolving goals, and support safe, personalized decision-making over time. Our findings underscore both the promise and the complexity of designing systems capable of supporting health trajectories beyond isolated interactions, and we offer guidance for future research and development in multi-session, user-centered health AI.

翻译：尽管人工智能智能体被越来越多地提出用于支持潜在的纵向健康任务（如症状管理、行为改变和患者支持），但当前大多数实现仍未能有效促进用户意图并建立问责机制。这与此前关于支持纵向需求的研究形成鲜明对比——在这些研究中，持续随访、连贯推理以及与个体目标的持续对齐对有效性和安全性至关重要。本文借鉴既有的临床与个人健康信息学框架，定义了如何利用AI智能体协调纵向健康交互的系统性方案。我们提出一个多层框架及其对应的智能体架构，该架构通过重复交互实现了适应性、连贯性、持续性与自主性的可操作化。通过代表性用例，我们展示了纵向智能体如何维持有意义的互动、适应不断演变的目标，并长期支持安全且个性化的决策。研究结果揭示了设计能够超越孤立交互、支持健康轨迹系统的潜力与复杂性，并为未来面向多会话、以用户为中心的健康AI研究与开发提供了指导。