2026
1.
Ranjan Sapkota; Konstantinos I. Roumeliotis; Manoj Karkee
AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges Journal Article
In: Information Fusion, vol. 126, pp. 103599, 2026, ISSN: 1566-2535.
Abstract | Links | BibTeX | Tags: Agentic AI, AI agents, Conceptual taxonomy, Context awareness, Multi-agent systems
@article{sapkota_ai_2026,
title = {AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges},
author = {Ranjan Sapkota and Konstantinos I. Roumeliotis and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S1566253525006712},
doi = {https://doi.org/10.1016/j.inffus.2025.103599},
issn = {1566-2535},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Information Fusion},
volume = {126},
pages = {103599},
abstract = {Information fusion, in the context of the Generative AI era, must distinguish AI Agents from Agentic AI. 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.},
keywords = {Agentic AI, AI agents, Conceptual taxonomy, Context awareness, Multi-agent systems},
pubstate = {published},
tppubtype = {article}
}
Information fusion, in the context of the Generative AI era, must distinguish AI Agents from Agentic AI. 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.
