A Call for Collaborative Intelligence: Why Human-Agent Systems Should
  Precede AI Autonomy
Authors: name: Henry Peng Zou, name: Wei-Chieh Huang, name: Yaozu Wu, name: Chunyu Miao, name: Dongyuan Li, name: Aiwei Liu, name: Yue Zhou, name: Yankai Chen, name: Weizhi Zhang, name: Yangning Li, name: Liancheng Fang, name: Renhe Jiang, name: Philip S. Yu

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.