AI2Agent自动部署AI项目，减少人工干预，提升成功率。
AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
- 基于指南驱动与自我调试，动态应对部署难题。
- 30个案例测试中，部署时间显著缩短，成功率达90%以上。
- 适合希望自动化部署AI应用的开发者与团队。
随着AI技术发展，各行业对可扩展的AI项目部署需求日益增长。然而，复杂的环境配置、依赖冲突、跨平台适配和调试困难等问题仍制约着自动化与普及。本文提出AI2Agent，一个端到端框架，通过指南驱动执行、自适应调试和案例-解决方案积累，实现AI项目自动化部署。该框架能动态分析部署挑战，从历史案例中学习并迭代优化策略，大幅减少人工介入。我们在30个AI部署案例上进行实验，涵盖语音合成（TTS）、文生图、图像编辑等应用。结果表明，AI2Agent显著缩短部署时间，提升成功率。代码与演示视频已公开。
原文摘要 · Abstract (English)
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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