Autonomous AI Agents for Workflow Optimization

  • Authors

    • Dr. Chen Wei Professor, Peking University, China. Author
    • Dr. Liu Fang Associate Professor, Tsinghua University, China. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2024PII5J8W

    Published 10-04-2024

  • Autonomous Ai Agents, Workflow Optimization, Intelligent Systems, Reinforcement Learning, Multi-Agent Systems, Process Automation, Decision Support Systems

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    C. Wei and L. Fang, “Autonomous AI Agents for Workflow Optimization”, IJAIDT, vol. 7, no. 2, pp. 01–15, Oct. 2024, doi: 10.67228/30713315/IJAIDT-2024PII5J8W.

  • Abstract

    Autonomous AI agents represent a major advancement in workflow optimization by enabling intelligent, adaptive, and self-learning automation. Unlike traditional rule-based systems, these agents can handle dynamic environments, uncertainty, and complex decision-making through techniques such as reinforcement learning and natural language processing. Their integration into enterprise workflows improves efficiency, reduces execution time, minimizes errors, and optimizes resource utilization. The study highlights that agent-based models significantly outperform conventional automation methods, especially in complex and changing conditions. Although challenges like scalability and ethical concerns remain, autonomous AI agents have strong potential to transform workflows into self-optimizing systems across various industries.

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