title: Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications
    of Agentic AI
summary: 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.
paper_authors._id: 683517bf6bb42c7e99bd3b5c
paper_authors.name: Ranjan Sapkota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T07:53:14.365Z
paper_authors._id: 67ddd80896ac367438d400a6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
paper_authors.fullname: Ranjan Sapkota
paper_authors.type: user
paper_authors.user: RanjanSapkota
paper_authors._id: 683517bf6bb42c7e99bd3b5d
paper_authors.name: Konstantinos I. Roumeliotis
paper_authors._id: 683517bf6bb42c7e99bd3b5e
paper_authors.name: Manoj Karkee
paper_title: Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications
    of Agentic AI
paper_summary: 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.
paper_ai_summary: A review contrasts vibe coding and agentic coding paradigms, highlighting their differences in interaction, autonomy, and application areas in AI-assisted software development.
paper_ai_keywords: large language models vibe coding agentic coding prompt-based conversational workflows goal-driven agents execution models feedback loops safety mechanisms debugging strategies tool ecosystems hybrid architectures autonomous execution pipelines trustworthy explainable collaborative systems unified human-centered development lifecycle