AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents

• URL: http://arxiv.org/abs/2503.23948v1
• Date: Mon, 31 Mar 2025 10:58:34 GMT
• Title: AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
• Authors: Jiaxiang Chen, Jingwei Shi, Lei Gan, Jiale Zhang, Qingyu Zhang, Dongqian Zhang, Xin Pang, Zhucong Li, Yinghui Xu,
• Abstract summary: This paper introduces AI2Agent, an end-to-end framework that automates AI project deployment through guideline-driven execution.<n>We conducted experiments on 30 AI deployment cases, covering TTS, text-to-image generation, image editing, and other AI applications.<n>Results show that AI2Agent significantly reduces deployment time and improves success rates.
• Score: 15.802600809497097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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