A Call for Collaborative Intelligence: Why Human-Agent Systems Should
  Precede AI Autonomy
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.
684ae20fdbd21a9cc27b0fed
Henry Peng Zou
claimed_verified
2025-06-13T07:41:15.323Z
633f112013e836a0fc4fa567
https://cdn-avatars.huggingface.co/v1/production/uploads/1665077519962-noauth.jpeg
Henry Peng Zou
user
TreeForest
684ae20fdbd21a9cc27b0fee
Wei-Chieh Huang
684ae20fdbd21a9cc27b0fef
Yaozu Wu
684ae20fdbd21a9cc27b0ff0
Chunyu Miao
684ae20fdbd21a9cc27b0ff1
Dongyuan Li
684ae20fdbd21a9cc27b0ff2
Aiwei Liu
684ae20fdbd21a9cc27b0ff3
Yue Zhou
684ae20fdbd21a9cc27b0ff4
Yankai Chen
684ae20fdbd21a9cc27b0ff5
Weizhi Zhang
684ae20fdbd21a9cc27b0ff6
Yangning Li
claimed_verified
2025-07-17T08:20:35.053Z
67931306b34bc03469a20fa7
/avatars/35780cbdbdfe9167c611ec4247d67cd4.svg
Yangning Li
user
liyn20
684ae20fdbd21a9cc27b0ff7
Liancheng Fang
684ae20fdbd21a9cc27b0ff8
Renhe Jiang
684ae20fdbd21a9cc27b0ff9
Philip S. Yu
A Call for Collaborative Intelligence: Why Human-Agent Systems Should
  Precede AI Autonomy
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.
The paper advocates for LLM-based Human-Agent Systems over fully autonomous AI agents due to improved reliability, transparency, and understanding of human needs through collaboration in various domains.
large language models LLM-based Human-Agent Systems human-agent teamwork healthcare finance software development