The emergence of large language model (LLM)-based agents and multi-agent systems has enabled a shift from narrow task automation to more autonomous decision-making. Despite progress in language generation, planning, tool use, and coordination, most agents still treat intelligence as prediction, optimization, and task completion. Human environments are social and normative, where people reason under bounded rationality, communicate in culturally situated language, and make decisions guided by values, beliefs, trust, and social norms. This survey argues that future AI agents, especially those acting on behalf of humans, must move beyond task competence toward human-centered capabilities. We review research across six areas: (1) evolution of intelligent agents, (2) human cognition and decision-making, (3) language, culture, and social context, (4) human values and belief systems, (5) human-agent collaboration, and (6) multi-agent coordination and modeling of human characteristics. We synthesize work from cognitive science, sociolinguistics, computational social science, and AI alignment, along with recent advances in LLM agents, cultural alignment benchmarks, preference learning, explainability, and agent societies. We identify a key gap: existing systems do not provide a unified framework integrating cognition, culture, values, and social behavior into autonomous agents. We conclude with directions for building culturally aware, value-aligned, cognitively grounded, and cooperative multi-agent systems.

翻译：基于大型语言模型（LLM）的智能体与多智能体系统的出现，推动了从狭义任务自动化向更自主决策的转变。尽管在语言生成、规划、工具使用与协调方面取得进展，大多数智能体仍将智能视为预测、优化与任务完成。人类环境具有社会性与规范性，人们在有限理性下进行推理，以具文化情境的语言沟通，并依据价值观、信念、信任及社会规范做出决策。本综述指出，未来的AI智能体，尤其是代表人类行动的智能体，必须超越任务能力，迈向以人为中心的能力。我们回顾了六大领域的研究：（1）智能体的演进，（2）人类认知与决策，（3）语言、文化与社会情境，（4）人类价值观与信念体系，（5）人机协作，以及（6）多智能体协调与人类特征建模。我们综合了认知科学、社会语言学、计算社会科学与AI对齐领域的工作，以及LLM智能体、文化对齐基准、偏好学习、可解释性与智能体社会的近期进展。我们识别出一个关键缺口：现有系统未能提供一个将认知、文化、价值观与社会行为整合到自主智能体中的统一框架。最后，我们提出了构建具有文化感知、价值观对齐、认知基础及协作能力的多智能体系统的未来方向。