A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy

作者：Henry Peng Zou, Wei Huang, Yaozu Wu, Miao, Chunyu, Dongyuan Li, Aiwei Liu, Yue Zhou, Yankai Chen, Weizhi Zhang, Yangning Li, Liancheng Fang, Renhe Jiang, Philip S. Yu · 发表于：arXiv (Cornell University) · 年份：2025 · DOI：10.48550/arxiv.2506.09420 · 被引用次数：1 · 研究领域：Ethics and Social Impacts of AI、Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)

Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach is the right path forward, as these autonomous systems still have problems with reliability, transparency, and understanding the actual requirements of human. We suggest a different approach: LLM-based Human-Agent Systems (LLM-HAS), where AI works with humans rather than replacing them. By keeping human involved to provide guidance, answer questions, and maintain control, these systems can be more trustworthy and adaptable. Looking at examples from healthcare, finance, and software development, we show how human-AI teamwork can handle complex tasks better than AI working alone. We also discuss the challenges of building these collaborative systems and offer practical solutions. This paper argues that progress in AI should not be measured by how independent systems become, but by how well they can work with humans. The most promising future for AI is not in systems that take over human roles, but in those that enhance human capabilities through meaningful partnership.