@inproceedings{zhong-etal-2026-enterpriseclawbench,
title = "{E}nterprise{C}law{B}ench: Building Enterprise Agent Benchmarks from Real Workplace Sessions",
author = "Zhong, Jincheng and
Wang, Weizhi and
Jiang, Che and
Tian, Kai and
Yuan, Zhenzhao and
Yang, Junlin and
Lei, Dianqiao and
Zhang, Kaiyan",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.219/",
pages = "3657--3672",
ISBN = "979-8-89176-460-6",
abstract = "Enterprise agents increasingly operate in workspaces, read heterogeneous files, use tools, and deliver business artifacts. We introduce EnterpriseClawBench, an internal enterprise agent benchmark constructed from proprietary agent sessions. Starting from a large archive of real workplace sessions, the EnterpriseClawBench pipeline produces 852 reproducible tasks with recovered fixtures, rewritten prompts, role classes, skill subclasses, hard rules, and semantic rubrics. Because the data contain internal enterprise content, the benchmark data are not released; the reusable contribution is the construction and evaluation protocol. On a manually audited 120-task Lite subset, the best score is only 0.663 for Codex with GPT-5.5. The results show that enterprise agent evaluation must report harness{--}model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than a single score."
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<abstract>Enterprise agents increasingly operate in workspaces, read heterogeneous files, use tools, and deliver business artifacts. We introduce EnterpriseClawBench, an internal enterprise agent benchmark constructed from proprietary agent sessions. Starting from a large archive of real workplace sessions, the EnterpriseClawBench pipeline produces 852 reproducible tasks with recovered fixtures, rewritten prompts, role classes, skill subclasses, hard rules, and semantic rubrics. Because the data contain internal enterprise content, the benchmark data are not released; the reusable contribution is the construction and evaluation protocol. On a manually audited 120-task Lite subset, the best score is only 0.663 for Codex with GPT-5.5. The results show that enterprise agent evaluation must report harness–model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than a single score.</abstract>
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%0 Conference Proceedings
%T EnterpriseClawBench: Building Enterprise Agent Benchmarks from Real Workplace Sessions
%A Zhong, Jincheng
%A Wang, Weizhi
%A Jiang, Che
%A Tian, Kai
%A Yuan, Zhenzhao
%A Yang, Junlin
%A Lei, Dianqiao
%A Zhang, Kaiyan
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F zhong-etal-2026-enterpriseclawbench
%X Enterprise agents increasingly operate in workspaces, read heterogeneous files, use tools, and deliver business artifacts. We introduce EnterpriseClawBench, an internal enterprise agent benchmark constructed from proprietary agent sessions. Starting from a large archive of real workplace sessions, the EnterpriseClawBench pipeline produces 852 reproducible tasks with recovered fixtures, rewritten prompts, role classes, skill subclasses, hard rules, and semantic rubrics. Because the data contain internal enterprise content, the benchmark data are not released; the reusable contribution is the construction and evaluation protocol. On a manually audited 120-task Lite subset, the best score is only 0.663 for Codex with GPT-5.5. The results show that enterprise agent evaluation must report harness–model combinations, artifact delivery, visual quality, cost, runtime, and skill-transfer behavior rather than a single score.
%U https://aclanthology.org/2026.emnlp-industry.219/
%P 3657-3672
Markdown (Informal)
[EnterpriseClawBench: Building Enterprise Agent Benchmarks from Real Workplace Sessions](https://aclanthology.org/2026.emnlp-industry.219/) (Zhong et al., EMNLP 2026)
ACL
- Jincheng Zhong, Weizhi Wang, Che Jiang, Kai Tian, Zhenzhao Yuan, Junlin Yang, Dianqiao Lei, and Kaiyan Zhang. 2026. EnterpriseClawBench: Building Enterprise Agent Benchmarks from Real Workplace Sessions. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3657–3672, Budapest, Hungary. Association for Computational Linguistics.