@inproceedings{chen-etal-2025-ai2agent,
title = "{AI}2{A}gent: An End-to-End Framework for Deploying {AI} Projects as Autonomous Agents",
author = "Chen, Jiaxiang and
Shi, Jingwei and
Gan, Lei and
Zhang, Jiale and
Zhang, Qingyu and
Zhang, Dongqian and
Xin, Pang and
Li, Zhucong and
Yinghui, Xu",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.51/",
doi = "10.18653/v1/2025.acl-demo.51",
pages = "535--541",
ISBN = "979-8-89176-253-4",
abstract = "As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption.This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case {\&} solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention.To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible."
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<abstract>As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption.This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case & solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention.To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.</abstract>
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%0 Conference Proceedings
%T AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
%A Chen, Jiaxiang
%A Shi, Jingwei
%A Gan, Lei
%A Zhang, Jiale
%A Zhang, Qingyu
%A Zhang, Dongqian
%A Xin, Pang
%A Li, Zhucong
%A Yinghui, Xu
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F chen-etal-2025-ai2agent
%X As AI technology advances, it is driving innovation across industries, increasing the demand for scalable AI project deployment. However, deployment remains a critical challenge due to complex environment configurations, dependency conflicts, cross-platform adaptation, and debugging difficulties, which hinder automation and adoption.This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution, self-adaptive debugging, and case & solution accumulation. AI2Agent dynamically analyzes deployment challenges, learns from past cases, and iteratively refines its approach, significantly reducing human intervention.To evaluate its effectiveness, we conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications. Results show that AI2Agent significantly reduces deployment time and improves success rates. The code and demo video are now publicly accessible.
%R 10.18653/v1/2025.acl-demo.51
%U https://aclanthology.org/2025.acl-demo.51/
%U https://doi.org/10.18653/v1/2025.acl-demo.51
%P 535-541
Markdown (Informal)
[AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents](https://aclanthology.org/2025.acl-demo.51/) (Chen et al., ACL 2025)
ACL
- Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Pang Xin, Zhucong Li, and Xu Yinghui. 2025. AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 535–541, Vienna, Austria. Association for Computational Linguistics.