A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Abstract
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.
Cite
@article{arxiv.2506.09420,
title = {A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy},
author = {Henry Peng Zou and Wei-Chieh Huang and Yaozu Wu and Chunyu Miao and Dongyuan Li and Aiwei Liu and Yue Zhou and Yankai Chen and Weizhi Zhang and Yangning Li and Liancheng Fang and Renhe Jiang and Philip S. Yu},
journal= {arXiv preprint arXiv:2506.09420},
year = {2025}
}