AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and
  Challenge
Authors: name: Ranjan Sapkota, name: Konstantinos I. Roumeliotis, name: Manoj Karkee

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