AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges

TL;DR

This paper offers a structured comparison of AI Agents and Agentic AI, detailing their architectures, applications, and challenges to clarify their differences and guide future development.

Contribution

It introduces a comprehensive taxonomy and analysis of AI Agents and Agentic AI, highlighting their architectural evolution, application domains, and unique challenges.

Findings

AI Agents are modular, task-specific systems driven by LLMs and LIMs.

Agentic AI involves multi-agent collaboration with dynamic task decomposition.

Distinct challenges include hallucination, brittleness, and coordination failure.

Abstract

This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges 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 and enabled by LLMs and LIMs for task-specific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then characterize Agentic AI systems, which, in contrast to AI Agents, represent a paradigm shift marked by multi-agent collaboration, dynamic task decomposition, persistent memory, and coordinated autonomy. Through a chronological evaluation of architectural evolution, operational mechanisms, interaction styles, and autonomy levels, we present a comparative analysis across both AI agents and agentic AI paradigms. Application domains enabled by AI Agents such as customer support, scheduling, and data summarization are then 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, retrieval-augmented generation (RAG), automation coordination layers, and causal modeling. This work aims to provide a roadmap for developing robust, scalable, and explainable AI-driven systems.