Agentic Artificial Intelligence Systems: The Emerging Role of AI in the Digital Economy

Article Sidebar

pdf
Published: Dec 29, 2025
Keywords:
Agentic AI Artificial Intelligence AI Ethics Transparency Digital Economy

Main Article Content

Adipon Euajarusphan
College of Innovation, Thammasat University, Thailand

Abstract

This article examines the role and challenges of Agentic AI in the digital economy. It highlights the paradigm shift from task-based AI-which relies on human commands for specific functions—to AI systems capable of autonomous goal setting, planning, and decision-making. The paper delineates the distinctive features of Agentic AI and critically examines associated ethical challenges, including transparency, trust, accountability, and fairness. A conceptual framework titled “CLEAR Framework” is proposed to guide the ethical design of Agentic AI systems. The framework consists of five key elements: Consent awareness, Lineage traceability, Explainability, Autonomy boundaries, and Responsibility loop. The article also presents real-world applications from sectors such as education, enterprise communication, and strategic management to demonstrate practical implications. Lastly, the paper offers policy recommendations for sustainable development of Agentic AI, emphasizing the importance of an “Ethics-by-Design” approach to ensure responsible integration of AI into socio-economic systems.

Article Details

How to Cite
Euajarusphan, A. (2025). Agentic Artificial Intelligence Systems: The Emerging Role of AI in the Digital Economy. Journal of Organizational Management and Social Development, 5(2), 141–154. retrieved from https://so17.tci-thaijo.org/index.php/JOMSD/article/view/1159
Issue
Vol. 5 No. 2: July-December 2025
Section
Academic Article

References

ชวลิต ศุภศักดิ์ธำรง. (2568ก). การวิเคราะห์องค์ประกอบเชิงยืนยันอันดับสองของโมเดลการวัดความท้าทายในการนำปัญญาประดิษฐ์มาใช้ในงานบริหารทรัพยากรมนุษย์ของอุตสาหกรรมไมซ์. วารสารสังคมศาสตร์ปัญญาพัฒน์, 7(3), 281-298.

ชวลิต ศุภศักดิ์ธำรง. (2568ข). ความสามารถด้านปัญญาประดิษฐ์และสมรรถนะด้านทรัพยากรมนุษย์ที่มีอิทธิพลต่อการพัฒนาประสิทธิภาพในการปฏิบัติงานอุตสาหกรรมยานยนต์ เขตพัฒนาพิเศษภาคตะวันออกของประเทศไทย. วารสารสังคมศาสตร์ปัญญาพัฒน์, 7(3), 1-16.

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019, May). Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-13). New York: Association for Computing Machinery (ACM).

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018, April). 'It's Reducing a Human Being to a Percentage' Perceptions of Justice in Algorithmic Decisions. In Proceedings of the 2018 Chi conference on human factors in computing systems (pp. 1-14). New York: Association for Computing Machinery (ACM).

Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, 4(1), 2-61.

Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way (Vol. 2156). Cham: Springer.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. Retrieved from https://arxiv.org/pdf/1702.08608.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Sharma, S. K., Singh, J. B., Raghavan, V., Raman, R., Rana, N. P., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.

EUROPEIA, C. (2021). Proposal for a Regulation on a European approach for Artificial Intelligence. Bruxelas: Comissão Europeia.

Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In Carta, S. (Ed.). Machine learning and the city: applications in architecture and urban design (pp. 535-545). New Jersey: John Wiley & Sons.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and machines, 28(4), 689-707.

Ghosh, U. K. (2025). Transformative AI Applications in Business Decision-Making: Advancing Data-Driven Strategies and Organizational Intelligence. In AI-Powered Leadership: Transforming Organizations in the Digital Age (pp. 1-40). Pennsylvania: IGI Global Scientific Publishing.

Guszcza, J., Lewis, H., & Evans-Greenwood, P. (2017). Cognitive collaboration: Why humans and computers think better together. Deloitte Review, 20, 8-29.

Holstein, K., McLaren, B. M., & Aleven, V. (2019, June). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. In International conference on artificial intelligence in education (pp. 157-171). Cham: Springer International Publishing.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399.

Lund, S., Madgavkar, A., Manyika, J., Smit, S., Ellingrud, K., Meaney, M., & Robinson, O. (2021). The future of work after COVID-19. Retrieved from https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19.

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM computing surveys (CSUR), 54(6), 1-35.

Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267, 1-38.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.

OECD. (2019). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments. Paris: OECD.

OECD. (2022). OECD framework for the classification of AI systems. Paris: OECD.

OECD. (2022). OECD recommendation on the ethics of artificial intelligence. Retrieved from https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449.

Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22). San Francisco: Association for Computing Machinery (ACM).

Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A. ‘Sandy’, Roberts, M. E., Shariff, A., Tenenbaum, J. B., & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.

Statista. (2023). Artificial Intelligence (AI) market revenue worldwide from 2018 to 2027. Hamburg: Statista Research Department.

Suresh, H., & Guttag, J. V. (2021). A framework for understanding unintended consequences of machine learning. Retrieved from https://courses.cs.duke.edu/compsci342/spring20/netid/readings/suresh-guttag-framework.pdf.

Thomas, R., Zikopoulos, P., & Soule, K. (2025). AI Value Creators. California: O’Reilly Media.

UNESCO. (2021). Recommendation on the ethics of artificial intelligence. Retrieved from https://www.unesco.org/en/articles/recommendation-ethics-artificial-intelligence.

Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2), 2053951717743530.

Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887.

Wilson, H. J., & Daugherty, P. R. (2018). Human + Machine: Reimagining Work in the Age of AI. Boston, MA: Harvard Business Review Press.

Zhou, J., Zhang, Y., Luo, Q., Parker, A. G., & De Choudhury, M. (2023, April). Synthetic lies: Understanding ai-generated misinformation and evaluating algorithmic and human solutions. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1-20). New York: Association for Computing Machinery (ACM).