AI2Agent: An End-to-End Framework for Deploying AI Projects as Autonomous Agents
This addresses deployment difficulties for AI practitioners, though it appears incremental as it builds on existing automation concepts.
The paper tackles the challenge of deploying AI projects by introducing AI2Agent, an end-to-end framework that automates deployment through guideline-driven execution and self-adaptive debugging, resulting in reduced deployment time and improved success rates in experiments on 30 AI cases.
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.