Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications
  of Agentic AI
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.
683517bf6bb42c7e99bd3b5c
Ranjan Sapkota
claimed_verified
2025-05-27T07:53:14.365Z
67ddd80896ac367438d400a6
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
Ranjan Sapkota
user
RanjanSapkota
683517bf6bb42c7e99bd3b5d
Konstantinos I. Roumeliotis
claimed_verified
2025-07-17T08:16:48.895Z
64bcce94f8f28a19b0ba6c86
/avatars/45316b6fd19886eb18ff669f08bdd311.svg
Dr. Konstantinos I. Roumeliotis
user
rkonstadinos
683517bf6bb42c7e99bd3b5e
Manoj Karkee
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications
  of Agentic AI
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.
A review contrasts vibe coding and agentic coding paradigms, highlighting their differences in interaction, autonomy, and application areas in AI-assisted software development.
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