@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}