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

2025-03-31

AI Summary

AI deployment faces critical bottlenecks—including complex environment configuration, dependency conflicts, cross-platform incompatibility, and inefficient debugging—severely hindering automation. To address these challenges, this paper introduces the first guideline-driven automation framework specifically designed for AI deployment. It integrates program analysis, case-based reasoning (CBR), self-supervised debugging feedback loops, and multimodal task abstraction modeling. Key innovations include guideline-driven execution, adaptive debugging, and a dynamic “case–solution” accumulation mechanism that enables continuous refinement of deployment strategies. Evaluated on 30 real-world AI tasks—including text-to-speech, text-to-image generation, and image editing—the framework reduces average deployment time by 68% and increases success rate to 96.7%, while significantly minimizing manual intervention. This work advances the paradigm of AI engineering toward systematic, automated, and iterative deployment.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Auto ML and Hyperparameter Tuning

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

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.

Problem

• Automates AI project deployment to reduce manual effort
• Solves dependency conflicts and cross-platform adaptation issues
• Improves deployment success rates through adaptive debugging

Innovation

• End-to-end framework for AI deployment
• Guideline-driven execution and self-adaptive debugging
• Dynamic analysis and iterative refinement