@inproceedings{zhu-etal-2025-oagents,
title = "{OA}gents: An Empirical Study of Building Effective Agents",
author = "Zhu, He and
Qin, Tianrui and
Zhu, King and
Huang, Heyuan and
Guan, Yeyi and
Xia, Jinxiang and
Li, Hanhao and
Yao, Yi and
Wang, Ningning and
Liu, Pai and
Peng, Tianhao and
Gui, Xin and
Xiaowan, Li and
Liu, Yuhui and
Tang, Xiangru and
Yang, Jian and
Zhang, Ge and
Gao, Xitong and
Jiang, Yuchen Eleanor and
Zhang, Changwang and
Wang, Jun and
Liu, Jiaheng and
Zhou, Wangchunshu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.720/",
pages = "13354--13369",
ISBN = "979-8-89176-335-7",
abstract = "Recently, Agentic AI has become an increasingly popular field of research. However, we argue that current practices on agent research are far from standard, rigorous scientific research, which makes it hard to conduct apples-to-apples comparisons among and against existing methods. As a result, it is still obscure how different design choices in an agent framework impact its effectiveness, and measuring progress on agent research remains very hard. In this work, we conduct a systematic empirical study on the GAIA benchmark to investigate the impact of different popular design choices within key agent components in a fair and rigorous way. To begin with, we find that the lack of a standard evaluation protocol makes previous works, even the open-sourced ones, not reproducible, and the variance between different random runs is often non-negligible. Therefore, we first introduce a more robust evaluation protocol to make comparisons more stable. Our empirical study then unveils which components and designs, as well as correlations between these designs, are the keys for building effective agents, while others are not and redundant, despite seemingly making sense. With the insights gained from our empirical study, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects, providing a good starting point and guidelines for building effective agents. More importantly, supports various design choices for agent components in a modularized way, facilitating future scientific research on Agentic AI."
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<abstract>Recently, Agentic AI has become an increasingly popular field of research. However, we argue that current practices on agent research are far from standard, rigorous scientific research, which makes it hard to conduct apples-to-apples comparisons among and against existing methods. As a result, it is still obscure how different design choices in an agent framework impact its effectiveness, and measuring progress on agent research remains very hard. In this work, we conduct a systematic empirical study on the GAIA benchmark to investigate the impact of different popular design choices within key agent components in a fair and rigorous way. To begin with, we find that the lack of a standard evaluation protocol makes previous works, even the open-sourced ones, not reproducible, and the variance between different random runs is often non-negligible. Therefore, we first introduce a more robust evaluation protocol to make comparisons more stable. Our empirical study then unveils which components and designs, as well as correlations between these designs, are the keys for building effective agents, while others are not and redundant, despite seemingly making sense. With the insights gained from our empirical study, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects, providing a good starting point and guidelines for building effective agents. More importantly, supports various design choices for agent components in a modularized way, facilitating future scientific research on Agentic AI.</abstract>
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%0 Conference Proceedings
%T OAgents: An Empirical Study of Building Effective Agents
%A Zhu, He
%A Qin, Tianrui
%A Zhu, King
%A Huang, Heyuan
%A Guan, Yeyi
%A Xia, Jinxiang
%A Li, Hanhao
%A Yao, Yi
%A Wang, Ningning
%A Liu, Pai
%A Peng, Tianhao
%A Gui, Xin
%A Xiaowan, Li
%A Liu, Yuhui
%A Tang, Xiangru
%A Yang, Jian
%A Zhang, Ge
%A Gao, Xitong
%A Jiang, Yuchen Eleanor
%A Zhang, Changwang
%A Wang, Jun
%A Liu, Jiaheng
%A Zhou, Wangchunshu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhu-etal-2025-oagents
%X Recently, Agentic AI has become an increasingly popular field of research. However, we argue that current practices on agent research are far from standard, rigorous scientific research, which makes it hard to conduct apples-to-apples comparisons among and against existing methods. As a result, it is still obscure how different design choices in an agent framework impact its effectiveness, and measuring progress on agent research remains very hard. In this work, we conduct a systematic empirical study on the GAIA benchmark to investigate the impact of different popular design choices within key agent components in a fair and rigorous way. To begin with, we find that the lack of a standard evaluation protocol makes previous works, even the open-sourced ones, not reproducible, and the variance between different random runs is often non-negligible. Therefore, we first introduce a more robust evaluation protocol to make comparisons more stable. Our empirical study then unveils which components and designs, as well as correlations between these designs, are the keys for building effective agents, while others are not and redundant, despite seemingly making sense. With the insights gained from our empirical study, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects, providing a good starting point and guidelines for building effective agents. More importantly, supports various design choices for agent components in a modularized way, facilitating future scientific research on Agentic AI.
%U https://aclanthology.org/2025.findings-emnlp.720/
%P 13354-13369
Markdown (Informal)
[OAgents: An Empirical Study of Building Effective Agents](https://aclanthology.org/2025.findings-emnlp.720/) (Zhu et al., Findings 2025)
ACL
- He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, and Wangchunshu Zhou. 2025. OAgents: An Empirical Study of Building Effective Agents. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13354–13369, Suzhou, China. Association for Computational Linguistics.