@inproceedings{zhang-etal-2026-clawbench,
title = "{C}law{B}ench: Can {AI} Agents Complete Everyday Online Tasks?",
author = "Zhang, Yuxuan and
Wang, Yubo and
Zhu, Yipeng and
Du, Penghui and
Miao, Junwen and
Lu, Xuan and
Li, Zhuofeng and
Qu, Xingwei and
Guo, Zhengkang and
Shen, Yuanzhe and
Song, Dingjie and
Zhou, Han and
Zheng, Tuney and
Wu, Xian and
Yu, Hao and
Cai, Songcheng and
Lu, Yi and
Hao, Yunzhuo and
Lei, Minyi and
Chen, Liang and
Zou, Kai and
Yin, Huifeng and
XU, Wendong and
Jiang, Dongfu and
Nie, Ping and
Liu, Jiaheng and
Chen, Wenhu and
Allen, Kelsey R",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.447/",
pages = "9386--9408",
ISBN = "979-8-89176-462-0",
abstract = "AI agents may assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating next-generation AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153 everyday online tasks common in daily life and work, spanning 144 platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and interaction challenges of real-world web environments. An interception layer captures and blocks the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 8 frontier models show that both proprietary and open-source models complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3{\%}, which exposes gaps in current AI agents. Progress on ClawBench brings us closer to AI agents that can function as general-purpose assistants."
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<abstract>AI agents may assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating next-generation AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153 everyday online tasks common in daily life and work, spanning 144 platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and interaction challenges of real-world web environments. An interception layer captures and blocks the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 8 frontier models show that both proprietary and open-source models complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%, which exposes gaps in current AI agents. Progress on ClawBench brings us closer to AI agents that can function as general-purpose assistants.</abstract>
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%0 Conference Proceedings
%T ClawBench: Can AI Agents Complete Everyday Online Tasks?
%A Zhang, Yuxuan
%A Wang, Yubo
%A Zhu, Yipeng
%A Du, Penghui
%A Miao, Junwen
%A Lu, Xuan
%A Li, Zhuofeng
%A Qu, Xingwei
%A Guo, Zhengkang
%A Shen, Yuanzhe
%A Song, Dingjie
%A Zhou, Han
%A Zheng, Tuney
%A Wu, Xian
%A Yu, Hao
%A Cai, Songcheng
%A Lu, Yi
%A Hao, Yunzhuo
%A Lei, Minyi
%A Chen, Liang
%A Zou, Kai
%A Yin, Huifeng
%A XU, Wendong
%A Jiang, Dongfu
%A Nie, Ping
%A Liu, Jiaheng
%A Chen, Wenhu
%A Allen, Kelsey R.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-clawbench
%X AI agents may assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating next-generation AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153 everyday online tasks common in daily life and work, spanning 144 platforms across 15 categories, from completing purchases and booking appointments to submitting job applications. These tasks require capabilities beyond existing benchmarks, such as obtaining relevant information from user-provided documents, navigating multi-step workflows across diverse platforms, and write-heavy operations like filling in many detailed forms correctly. Unlike existing benchmarks that evaluate agents in offline sandboxes with static pages, ClawBench operates on production websites, preserving the full complexity, dynamic nature, and interaction challenges of real-world web environments. An interception layer captures and blocks the final submission request, ensuring safe evaluation without real-world side effects. Our evaluations of 8 frontier models show that both proprietary and open-source models complete only a small portion of these tasks. For example, Claude Sonnet 4.6 achieves only 33.3%, which exposes gaps in current AI agents. Progress on ClawBench brings us closer to AI agents that can function as general-purpose assistants.
%U https://aclanthology.org/2026.findings-emnlp.447/
%P 9386-9408
Markdown (Informal)
[ClawBench: Can AI Agents Complete Everyday Online Tasks?](https://aclanthology.org/2026.findings-emnlp.447/) (Zhang et al., Findings 2026)
ACL
- Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, Junwen Miao, Xuan Lu, Zhuofeng Li, Xingwei Qu, Zhengkang Guo, Yuanzhe Shen, Dingjie Song, Han Zhou, Tuney Zheng, Xian Wu, Hao Yu, Songcheng Cai, Yi Lu, Yunzhuo Hao, Minyi Lei, Liang Chen, Kai Zou, Huifeng Yin, Wendong XU, Dongfu Jiang, Ping Nie, Jiaheng Liu, Wenhu Chen, and Kelsey R Allen. 2026. ClawBench: Can AI Agents Complete Everyday Online Tasks?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 9386–9408, Budapest, Hungary. Association for Computational Linguistics.