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AI Agents and Agentic Systems: A Multi-Expert Analysis

Laurie Hughes, Yogesh Dwivedi, Tegwen Malik Orcid Logo, Mazen Shawosh, Mousa Ahmed Albashrawi, Il Jeon, Vincent Dutot, Mandanna Appanderanda, Tom Crick Orcid Logo, Rahul De’, Mark Fenwick, Senali Madugoda Gunaratnege, Paulius Jurcys, Arpan Kumar Kar, Nir Kshetri, Keyao Li, Sashah Mutasa, Spyridon Samothrakis, Michael Wade, Paul Walton

Journal of Computer Information Systems, Volume: 65, Issue: 4, Pages: 489 - 517

Swansea University Authors: Yogesh Dwivedi, Tegwen Malik Orcid Logo, Tom Crick Orcid Logo

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DOI (Published version): 10.1080/08874417.2025.2483832

Abstract

The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potenti...

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Published in: Journal of Computer Information Systems
ISSN: 0887-4417 2380-2057
Published: Informa UK Limited 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69139
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spelling 2025-07-31T17:25:13.8091280 v2 69139 2025-03-24 AI Agents and Agentic Systems: A Multi-Expert Analysis d154596e71b99ad1285563c8fdd373d7 Yogesh Dwivedi Yogesh Dwivedi true false d7e74f3c3979dff2baba1a16fe50e24a 0000-0003-4315-5726 Tegwen Malik Tegwen Malik true false 200c66ef0fc55391f736f6e926fb4b99 0000-0001-5196-9389 Tom Crick Tom Crick true false 2025-03-24 The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potential of AI agents and agentic systems to reshape industries by decentralizing decision-making, redefining organizational structures, and enhancing cross-functional collaboration. Specific applications include healthcare systems capable of creating adaptive treatment plans, supply chain agents that predict and address disruptions in real-time, and business process automation that reallocates tasks from humans to AI, improving efficiency and innovation. However, the integration of these systems raises critical challenges, including issues of attribution and shared accountability in decision-making, compatibility with legacy systems, and addressing biases in AI-driven processes. The paper concludes that while agentic systems hold immense promise, robust governance frameworks, cross-industry collaboration, and interdisciplinary research into ethical design are essential. Future research should explore adaptive workforce reskilling strategies, transparent accountability mechanisms, and energy-efficient deployment models to ensure ethical and scalable implementation. Journal Article Journal of Computer Information Systems 65 4 489 517 Informa UK Limited 0887-4417 2380-2057 AI agents; agentic AI; agentic system; autonomous agent; cognitive agent; intelligent agent; OpenAI operator; smart agent; virtual assistant 4 7 2025 2025-07-04 10.1080/08874417.2025.2483832 COLLEGE NANME COLLEGE CODE Swansea University Not Required 2025-07-31T17:25:13.8091280 2025-03-24T08:56:58.4885477 Faculty of Humanities and Social Sciences School of Management - Business Management Laurie Hughes 1 Yogesh Dwivedi 2 Tegwen Malik 0000-0003-4315-5726 3 Mazen Shawosh 4 Mousa Ahmed Albashrawi 5 Il Jeon 6 Vincent Dutot 7 Mandanna Appanderanda 8 Tom Crick 0000-0001-5196-9389 9 Rahul De’ 10 Mark Fenwick 11 Senali Madugoda Gunaratnege 12 Paulius Jurcys 13 Arpan Kumar Kar 14 Nir Kshetri 15 Keyao Li 16 Sashah Mutasa 17 Spyridon Samothrakis 18 Michael Wade 19 Paul Walton 20 69139__33977__65a9125caf554a429bee912a626ff5ca.pdf 69139.AAM.pdf 2025-04-09T15:37:40.2704331 Output 773157 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title AI Agents and Agentic Systems: A Multi-Expert Analysis
spellingShingle AI Agents and Agentic Systems: A Multi-Expert Analysis
Yogesh Dwivedi
Tegwen Malik
Tom Crick
title_short AI Agents and Agentic Systems: A Multi-Expert Analysis
title_full AI Agents and Agentic Systems: A Multi-Expert Analysis
title_fullStr AI Agents and Agentic Systems: A Multi-Expert Analysis
title_full_unstemmed AI Agents and Agentic Systems: A Multi-Expert Analysis
title_sort AI Agents and Agentic Systems: A Multi-Expert Analysis
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
d7e74f3c3979dff2baba1a16fe50e24a
200c66ef0fc55391f736f6e926fb4b99
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
d7e74f3c3979dff2baba1a16fe50e24a_***_Tegwen Malik
200c66ef0fc55391f736f6e926fb4b99_***_Tom Crick
author Yogesh Dwivedi
Tegwen Malik
Tom Crick
author2 Laurie Hughes
Yogesh Dwivedi
Tegwen Malik
Mazen Shawosh
Mousa Ahmed Albashrawi
Il Jeon
Vincent Dutot
Mandanna Appanderanda
Tom Crick
Rahul De’
Mark Fenwick
Senali Madugoda Gunaratnege
Paulius Jurcys
Arpan Kumar Kar
Nir Kshetri
Keyao Li
Sashah Mutasa
Spyridon Samothrakis
Michael Wade
Paul Walton
format Journal article
container_title Journal of Computer Information Systems
container_volume 65
container_issue 4
container_start_page 489
publishDate 2025
institution Swansea University
issn 0887-4417
2380-2057
doi_str_mv 10.1080/08874417.2025.2483832
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description The emergence of AI agents and agentic systems represents a significant milestone in artificial intelligence, enabling autonomous systems to operate, learn, and collaborate in complex environments with minimal human intervention. This paper, drawing on multi-expert perspectives, examines the potential of AI agents and agentic systems to reshape industries by decentralizing decision-making, redefining organizational structures, and enhancing cross-functional collaboration. Specific applications include healthcare systems capable of creating adaptive treatment plans, supply chain agents that predict and address disruptions in real-time, and business process automation that reallocates tasks from humans to AI, improving efficiency and innovation. However, the integration of these systems raises critical challenges, including issues of attribution and shared accountability in decision-making, compatibility with legacy systems, and addressing biases in AI-driven processes. The paper concludes that while agentic systems hold immense promise, robust governance frameworks, cross-industry collaboration, and interdisciplinary research into ethical design are essential. Future research should explore adaptive workforce reskilling strategies, transparent accountability mechanisms, and energy-efficient deployment models to ensure ethical and scalable implementation.
published_date 2025-07-04T05:23:09Z
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score 11.089718

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