The rapid advancement of AI, including Large Language Models, has propelled autonomous agents forward, accelerating the human-agent teaming (HAT) paradigm to leverage complementary strengths. However, HAT research remains fragmented, often focusing on isolated team development phases or specific challenges like trust calibration while overlooking the real-world need for adaptability. Addressing these gaps, a process dynamics perspective is adopted to systematically review HAT using the T$^4$ framework: Team Formation, Task and Role Development, Team Development, and Team Improvement. Each phase is examined in terms of its goals, actions, and evaluation metrics, emphasizing the co-evolution of task and team dynamics. Special focus is given to the second and third phases, highlighting key factors such as team roles, shared mental model, and backup behaviors. This holistic perspective identifies future research directions for advancing long-term adaptive HAT.

翻译：以大型语言模型为代表的人工智能技术快速发展，推动了自主智能体的进步，加速了人机协作（HAT）范式的发展，以利用人类与智能体之间的互补优势。然而，现有HAT研究仍较为零散，通常聚焦于孤立的团队发展阶段或特定挑战（如信任校准），而忽视了现实世界对适应性的需求。为弥补这些不足，本文采用过程动力学视角，运用T$^4$框架——团队组建、任务与角色发展、团队发展及团队改进——对HAT研究进行系统性综述。针对每个阶段，本文从目标、行动和评估指标等方面进行考察，强调任务动态与团队动态的协同演化。研究特别关注第二和第三阶段，重点分析了团队角色、共享心智模型及后备行为等关键因素。这一整体性视角为推进长期自适应HAT的未来研究方向提供了重要参考。