振动编码与代理编码：代理式AI的基础原理与实践意义

Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

May 26, 2025

作者: Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee

cs.AI

摘要

本综述对AI辅助软件开发中的两大新兴范式——氛围编码与代理编码——进行了全面分析。尽管两者均依托于大型语言模型（LLMs），但在自主性、架构设计及开发者角色上存在本质差异。氛围编码强调通过基于提示的对话式工作流实现直觉化的人机交互，支持构思、实验与创意探索。相比之下，代理编码则通过目标驱动的代理实现自主软件开发，这些代理能够规划、执行、测试并迭代任务，所需人工干预极少。我们提出了一套详尽的分类体系，涵盖概念基础、执行模型、反馈循环、安全机制、调试策略及现实工具生态系统。通过对比工作流分析及20个详细用例，我们展示了氛围系统在早期原型设计与教育领域的优势，而代理系统则在企业级自动化、代码库重构及CI/CD集成方面表现卓越。此外，我们探讨了混合架构中的新兴趋势，即自然语言接口与自主执行管道的结合。最后，我们为代理式AI的未来发展绘制了路线图，概述了构建可信、可解释、协作系统所需的基础设施。我们的研究结果表明，成功的AI软件工程不在于选择单一范式，而在于在统一的人本开发生命周期中协调二者的优势。

English

This review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding. While both leverage large language models (LLMs), they differ fundamentally in autonomy, architectural design, and the role of the developer. Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational workflows that support ideation, experimentation, and creative exploration. In contrast, agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention. We propose a detailed taxonomy spanning conceptual foundations, execution models, feedback loops, safety mechanisms, debugging strategies, and real-world tool ecosystems. Through comparative workflow analysis and 20 detailed use cases, we illustrate how vibe systems thrive in early-stage prototyping and education, while agentic systems excel in enterprise-grade automation, codebase refactoring, and CI/CD integration. We further examine emerging trends in hybrid architectures, where natural language interfaces are coupled with autonomous execution pipelines. Finally, we articulate a future roadmap for agentic AI, outlining the infrastructure needed for trustworthy, explainable, and collaborative systems. Our findings suggest that successful AI software engineering will rely not on choosing one paradigm, but on harmonizing their strengths within a unified, human-centered development lifecycle.