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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2506.09420 |
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| _version_ | 1866909645939933184 |
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| author | Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Miao, Chunyu Li, Dongyuan Liu, Aiwei Zhou, Yue Chen, Yankai Zhang, Weizhi Li, Yangning Fang, Liancheng Jiang, Renhe Yu, Philip S. |
| author_facet | Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Miao, Chunyu Li, Dongyuan Liu, Aiwei Zhou, Yue Chen, Yankai Zhang, Weizhi Li, Yangning Fang, Liancheng Jiang, Renhe Yu, Philip S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09420 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy Zou, Henry Peng Huang, Wei-Chieh Wu, Yaozu Miao, Chunyu Li, Dongyuan Liu, Aiwei Zhou, Yue Chen, Yankai Zhang, Weizhi Li, Yangning Fang, Liancheng Jiang, Renhe Yu, Philip S. Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning Multiagent Systems 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. |
| title | A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2506.09420 |