[2608.17800] StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows

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arXiv:2608.17800 (cs)

[Submitted on 18 Aug 2026]

Title:StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows

Authors:Liya Zhu, Xin Ma, Tao Liu, Haodong Wang, Ge Zhang, Jingzhe Ding, Qingshui Gu, Yongjie Zhong, Jinxiang Meng, Yuan Gao, Yunqiu Zhou, Hao Zhu, Jifeng He, Yongzhi Liao, Xinyi Zhang, Chaoxin Li, Yi Zhu, Xi Lin, Duju Zeng, Xiang Gao, Wen Zhang, Yunyang Wang, Duo Wang, Huan Zhou, Zuo Wang, Jin Chen, Kaiyuan Zhang, Chuqian Yu, Tianhao Yu, Longxiang Liu, Jianbo Xue, Huimin Che, Jiahao Wang, Yujia Qin, Jiaheng Liu, Shen Yan, Xiaolong Chang, Wenhao Huang<br>View a PDF of the paper titled StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows, by Liya Zhu and 37 other authors

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Abstract:Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Yet existing benchmarks largely rely on researcher-selected tasks, leaving uncertain whether such progress extends to the work that real-world users actually demand from AI systems. We introduce \textbf{StartupBench}, an E2E agent benchmark grounded in market-validated AI startup products. Rather than defining tasks from pre-defined assumptions about useful agent capabilities, we systematically study AI products with demonstrated adoption, together with their product workflows and users, to identify real-world tasks for which AI has established practical demand across diverse professional domains. We translate these workflows into complete deliverable-oriented tasks and evaluate them with fine-grained rubrics capturing their complex requirements. Across representative models evaluated under a unified agent harness, even the strongest model successfully completes only approximately 30\% of StartupBench, despite making substantial partial progress on many tasks. Further analysis identifies aspects like complex instruction following and domain-specific expertise as major sources of failure. Our results reveal that many market-validated workflows remain beyond the reliable capabilities of current general-purpose agents, establishing StartupBench as an empirical measure of progress toward E2E completions of real-world user tasks.

Subjects:

Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2608.17800 [cs.AI]

(or<br>arXiv:2608.17800v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.17800

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Jingzhe Ding [view email]<br>[v1]<br>Tue, 18 Aug 2026 14:01:32 UTC (5,347 KB)

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