HUMAN-AI COLLABORATION: BALANCING AGENTIC AI AND AUTONOMY IN HYBRID SYSTEMS

In this article, the author explores the tension between the human factor and artificial intelligence as a symbiosis of two effective approaches to solving multifaceted, realistic tasks. Considering the premises of human-AI cooperation, it identifies how combined structures can improve these processes as decision making, scalability and flexibility in spheres including healthcare, auto transport industry as well as education. The discussion combines theories and case studies to explain how hybrid systems may retain transparent, fair, and ethical procedures while achieving operational performance. Beneficial samples include one focusing on developing possible issues with the implementation of, for instance, human supervision of AI and the growth of AI decision making self-governance, the problem of AI biases, and others pertaining to drawbacks of over-essentialization of AI.

Authors

Publication Details

Journal
International Journal on Cloud Computing Services and Architecture
Published
2025-02-27
DOI
https://doi.org/10.5121/ijccsa.2025.15101
Citations
2
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
4.96
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article

HUMAN-AI COLLABORATION: BALANCING AGENTIC AI AND AUTONOMY IN HYBRID SYSTEMS

G. M. Viswanathan, Gaurav Samdani, Abirami Dasu Jegadeesh
2 citations
International Journal on Cloud Computing Services and Architecture
Reinforcement Learning in Robotics
4.96
article

HUMAN-AI COLLABORATION: BALANCING AGENTIC AI AND AUTONOMY IN HYBRID SYSTEMS

G. M. Viswanathan, Gaurav Samdani, Abirami Dasu Jegadeesh
article en
2 citations

Abstract

In this article, the author explores the tension between the human factor and artificial intelligence as a symbiosis of two effective approaches to solving multifaceted, realistic tasks. Considering the premises of human-AI cooperation, it identifies how combined structures can improve these processes as decision making, scalability and flexibility in spheres including healthcare, auto transport industry as well as education. The discussion combines theories and case studies to explain how hybrid systems may retain transparent, fair, and ethical procedures while achieving operational performance. Beneficial samples include one focusing on developing possible issues with the implementation of, for instance, human supervision of AI and the growth of AI decision making self-governance, the problem of AI biases, and others pertaining to drawbacks of over-essentialization of AI.

International Journal on Cloud Computing Services and ArchitectureVol. 15(1)
Openalex Percentile: Top 5%
Reinforcement Learning in Robotics
4.96
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HUMAN-AI COLLABORATION: BALANCING AGENTIC AI AND AUTONOMY IN HYBRID SYSTEMS — G. M. Viswanathan, Gaurav Samdani, et al. · International Journal on Cloud Computing Services and Architecture (2025) | TGRS Research Map | TGRS