title: AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and
    Challenge
summary: This study critically distinguishes between AI Agents and Agentic AI,
  offering a structured conceptual taxonomy, application mapping, and challenge
  analysis to clarify their divergent design philosophies and capabilities. We
  begin by outlining the search strategy and foundational definitions,
  characterizing AI Agents as modular systems driven by Large Language Models
  (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation.
  Generative AI is positioned as a precursor, with AI Agents advancing through
  tool integration, prompt engineering, and reasoning enhancements. In contrast,
  Agentic AI systems represent a paradigmatic shift marked by multi-agent
  collaboration, dynamic task decomposition, persistent memory, and orchestrated
  autonomy. Through a sequential evaluation of architectural evolution,
  operational mechanisms, interaction styles, and autonomy levels, we present a
  comparative analysis across both paradigms. Application domains such as
  customer support, scheduling, and data summarization are contrasted with
  Agentic AI deployments in research automation, robotic coordination, and
  medical decision support. We further examine unique challenges in each paradigm
  including hallucination, brittleness, emergent behavior, and coordination
  failure and propose targeted solutions such as ReAct loops, RAG, orchestration
  layers, and causal modeling. This work aims to provide a definitive roadmap for
  developing robust, scalable, and explainable AI agent and Agentic AI-driven
  systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision
  Support System, Agentic-AI Applications
paper_authors._id: 682733b0d7b648c3eb78c9fd
paper_authors.name: Ranjan Sapkota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-16T13:23:12.733Z
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: 682733b0d7b648c3eb78c9fe
paper_authors.name: Konstantinos I. Roumeliotis
paper_authors._id: 682733b0d7b648c3eb78c9ff
paper_authors.name: Manoj Karkee
paper_title: AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and
    Challenge
paper_summary: This study critically distinguishes between AI Agents and Agentic AI,
  offering a structured conceptual taxonomy, application mapping, and challenge
  analysis to clarify their divergent design philosophies and capabilities. We
  begin by outlining the search strategy and foundational definitions,
  characterizing AI Agents as modular systems driven by Large Language Models
  (LLMs) and Large Image Models (LIMs) for narrow, task-specific automation.
  Generative AI is positioned as a precursor, with AI Agents advancing through
  tool integration, prompt engineering, and reasoning enhancements. In contrast,
  Agentic AI systems represent a paradigmatic shift marked by multi-agent
  collaboration, dynamic task decomposition, persistent memory, and orchestrated
  autonomy. Through a sequential evaluation of architectural evolution,
  operational mechanisms, interaction styles, and autonomy levels, we present a
  comparative analysis across both paradigms. Application domains such as
  customer support, scheduling, and data summarization are contrasted with
  Agentic AI deployments in research automation, robotic coordination, and
  medical decision support. We further examine unique challenges in each paradigm
  including hallucination, brittleness, emergent behavior, and coordination
  failure and propose targeted solutions such as ReAct loops, RAG, orchestration
  layers, and causal modeling. This work aims to provide a definitive roadmap for
  developing robust, scalable, and explainable AI agent and Agentic AI-driven
  systems. >AI Agents, Agent-driven, Vision-Language-Models, Agentic AI Decision
  Support System, Agentic-AI Applications
paper_ai_summary: The study differentiates AI Agents and Agentic AI through their design philosophies, capabilities, and applications, offering a taxonomy, mapping, and analysis of their challenges and solutions.
paper_ai_keywords: Large Language Models Large Image Models Generative AI multi-agent collaboration dynamic task decomposition persistent memory orchestrated autonomy ReAct loops RAG orchestration layers causal modeling