Position: The Real Barrier to LLM Agent Usability is Agentic ROI
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910011870937088 |
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| author | Liu, Weiwen Qin, Jiarui Huang, Xu Zeng, Xingshan Xi, Yunjia Lin, Jianghao Wu, Chuhan Wang, Yasheng Shang, Lifeng Tang, Ruiming Lian, Defu Yu, Yong Zhang, Weinan |
| author_facet | Liu, Weiwen Qin, Jiarui Huang, Xu Zeng, Xingshan Xi, Yunjia Lin, Jianghao Wu, Chuhan Wang, Yasheng Shang, Lifeng Tang, Ruiming Lian, Defu Yu, Yong Zhang, Weinan |
| contents | Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, planning, and goal-directed action. While LLM agents are technically capable of performing a broad range of tasks, not all of these capabilities translate into meaningful usability. This position paper argues that the central question for LLM agent usability is no longer whether a task can be automated, but whether it delivers sufficient Agentic Return on Investment (Agentic ROI). Agentic ROI reframes evaluation from raw performance to a holistic, utility-driven perspective, guiding when, where, and for whom LLM agents should be deployed. Despite widespread application in high-ROI tasks like coding and scientific research, we identify a critical usability gap in mass-market, everyday applications. To address this, we propose a zigzag developmental trajectory: first scaling up to improve information gain and time savings, then scaling down to reduce cost. We present a strategic roadmap across these phases to make LLM agents truly usable, accessible, and scalable in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17767 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Position: The Real Barrier to LLM Agent Usability is Agentic ROI Liu, Weiwen Qin, Jiarui Huang, Xu Zeng, Xingshan Xi, Yunjia Lin, Jianghao Wu, Chuhan Wang, Yasheng Shang, Lifeng Tang, Ruiming Lian, Defu Yu, Yong Zhang, Weinan Computation and Language Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, planning, and goal-directed action. While LLM agents are technically capable of performing a broad range of tasks, not all of these capabilities translate into meaningful usability. This position paper argues that the central question for LLM agent usability is no longer whether a task can be automated, but whether it delivers sufficient Agentic Return on Investment (Agentic ROI). Agentic ROI reframes evaluation from raw performance to a holistic, utility-driven perspective, guiding when, where, and for whom LLM agents should be deployed. Despite widespread application in high-ROI tasks like coding and scientific research, we identify a critical usability gap in mass-market, everyday applications. To address this, we propose a zigzag developmental trajectory: first scaling up to improve information gain and time savings, then scaling down to reduce cost. We present a strategic roadmap across these phases to make LLM agents truly usable, accessible, and scalable in real-world applications. |
| title | Position: The Real Barrier to LLM Agent Usability is Agentic ROI |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.17767 |