Autonomy, from the Greek autos (self) and nomos (law), refers to the capacity to operate according to internal rules without external control. Autonomous vehicles (AuVs) are therefore understood as systems that perceive their environment and execute pre-programmed tasks independently of external input, consistent with the SAE levels of automated driving. Yet recent research and real-world deployments have begun to showcase vehicles that exhibit behaviors outside the scope of this definition. These include natural language interaction with humans, goal adaptation, contextual reasoning, external tool use, and the handling of unforeseen ethical dilemmas, enabled in part by multimodal large language models (LLMs). These developments highlight not only a gap between technical autonomy and the broader cognitive and social capacities required for human-centered mobility, but also the emergence of a form of vehicle intelligence that currently lacks a clear designation. To address this gap, the paper introduces the concept of agentic vehicles (AgVs): vehicles that integrate agentic AI systems to reason, adapt, and interact within complex environments. It synthesizes recent advances in agentic systems and suggests how AgVs can complement and even reshape conventional autonomy to ensure mobility services are aligned with user and societal needs. The paper concludes by outlining key challenges in the development and governance of AgVs and their potential role in shaping future agentic transportation systems.

翻译：自主性（Autonomy）源于希腊语autos（自我）与nomos（法则），指依据内部规则运行且无需外部控制的能力。因此，自动驾驶车辆（AuVs）被理解为能够感知环境并独立于外部输入执行预设任务的系统，这符合SAE自动驾驶分级标准。然而，近期研究与实际部署已开始展现出超越此定义范畴的车辆行为，包括与人类进行自然语言交互、目标自适应调整、情境推理、外部工具使用以及处理未预见的伦理困境——这些能力部分得益于多模态大语言模型（LLMs）的赋能。这些进展不仅凸显了技术自主性与以人为本的移动出行所需更广泛认知及社会能力之间的差距，更揭示了一种尚未被明确定义的新型车辆智能形态。为弥合此差距，本文提出智能体车辆（AgVs）概念：即通过集成智能体人工智能系统，使其能够在复杂环境中进行推理、适应与交互的车辆。本文系统梳理了智能体系统的最新进展，探讨AgVs如何补充乃至重塑传统自主性技术，以确保移动出行服务契合用户与社会需求。最后，本文阐述了AgVs发展与管理中的关键挑战，并展望其在未来智能体交通系统中的潜在作用。