The Rise of Agentic AI: Redefining Human–Machine Collaboration
DOI:
https://doi.org/10.15662/IJARCST.2025.0805005Keywords:
Agentic AI, Machine Learning, Autonomous Vehicles, Healthcare AI, Human-Machine Collaboration, Decision-Making AccuracyAbstract
The emergence of agentic AI is an important change in how people and machines can work together, as AI systems no longer will a passive tool, but rather become independent agents taking decisions and improving productivity. This paper discusses the impact of agentic AI and its self-directed behavior and decision-making skills on the conventional concepts of human-computer interaction. In the research, the possibility to implement agentic AI and restructure such spheres as healthcare, transport, and finance in terms of the understanding of the actual applications and theoretical paradigms is identified. It also discusses the moral and real-life consequences of autonomous AI decision making. The findings show that agentic AI can potentially have positive effects, such as increased efficiency and accuracy during decision making, yet its use in human processes introduces certain problems, including control, accountability and trust. Such dynamics can be critical in the future of AI collaboration as the technology might be a good to society but a solution to the problems being experienced.
References
[1] Cheong, B. C. (2024). Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6. https://doi.org/10.3389/fhumd.2024.1421273
[2] Dattathrani, S., & De’, R. (2022). The Concept of Agency in the Era of Artificial Intelligence: Dimensions and Degrees. Information Systems Frontiers. https://doi.org/10.1007/s10796-022-10336-8
[3] Hu, S., Lu, C., & Clune, J. (2024). Automated Design of Agentic Systems. ArXiv.org. https://arxiv.org/abs/2408.08435
[4] Nalage, P. (2024). Leveraging Generative AI for Code Refactoring: A Study on Efficiency, Maintainability, and Developer Productivity. Well Testing Journal, 33(S2), 733-753.
[5] Rashid, M. H. (2024). Handbook of Power Electronics in Autonomous and Electric Vehicles. Elsevier.
[6] Ren, M., Chen, N., & Qiu, H. (2023). Human-machine Collaborative Decision-making: An Evolutionary Roadmap Based on Cognitive Intelligence. International Journal of Social Robotics, 15(7), 1101–1114. https://doi.org/10.1007/s12369-023-01020-1
[7] Steels, L. (2018). Building Agents out of Autonomous Behavior Systems. Routledge EBooks, 83–121. https://doi.org/10.4324/9781351001885-3.
[8] Nalage, P. (2025). Agentic Digital Twins: Self-Evolving Models for Autonomous Systems. Well Testing Journal, 34(S3), 227-244.
[9] van Ginneken, B. (2017). Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiological Physics and Technology, 10(1), 23–32. https://doi.org/10.1007/s12194-017-0394-5
