Over the last couple of years, AI Agents have gained significant traction due to substantial progress in the capabilities of underlying General Purpose AI (GPAI) models, enhanced scaffolding techniques, and the promise to drive societal transformation. Companies, researchers, and policy makers have started to consider the different effects that AI agents may have across different dimensions of our lives. However, the literature exploring the broader effects of human-agent interactions is still underdeveloped. In this paper, we review the problem of traffic modulation by autonomous vehicles (AVs) in mixed traffic flows and extrapolate the learnings to the different modes of interaction between humans and AVs to the pair humans-AI agents. In doing so, we propose a preliminary taxonomy of relational archetypes based on literature on Human-Computer Interaction (HCI) and AV-human interaction and tentatively explore how the resulting framework may lead to new questions regarding human-agent interactions. Our effort is aimed at strengthening existing bridges between these two research communities, which share similar traits: autonomy, fast adoption, high impact, and great potential for economic transformation. Building on previous analogies between AI Agents and AVs (e.g., regarding autonomy levels), we anticipate this paper to spark scholarly debate on the different types of impact that agents may have on our societies, while inviting other researchers to expand the scope of their comparative analysis regarding AI Agents.

翻译：过去几年中，AI智能体因通用人工智能（GPAI）模型能力的显著进步、增强的支撑技术以及推动社会变革的潜力而备受关注。企业、研究者和政策制定者已开始考虑AI智能体可能在我们生活不同维度产生的多元影响。然而，探索人-智能体交互更广泛效应的文献仍显不足。本文首先审视了混合交通流中自动驾驶汽车（AVs）的交通调控问题，继而将人-车交互模式的经验推广至人-AI智能体交互领域。基于此，我们依据人机交互（HCI）与AV-人类交互文献，提出关系原型的初步分类体系，并尝试探索该框架如何引发关于人-智能体交互的新思考。本研究旨在强化两个研究社群间的现有桥梁——二者共享自主性、快速应用、高影响力及巨大经济转型潜力等特征。通过借鉴此前关于AI智能体与AV的类比（如自主性等级），本文预期能引发学界对智能体在社会中不同类型影响的学术讨论，同时邀请其他研究者拓展其关于AI智能体的比较分析范畴。