Agentic AI Architectures for Autonomous Business Applications

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India Author
    • P. K. Iyengar Scientific Computing Researcher, BARC, India Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2024PI9Y4N

    Published 01-05-2024

  • Agentic AI, Autonomous Systems, Business Intelligence, Multi-Agent Systems, Enterprise AI, Autonomous Business Applications, Decision Intelligence, Intelligent Automation

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar and P. K. Iyengar, “Agentic AI Architectures for Autonomous Business Applications”, ijmiet, vol. 7, no. 1, pp. 01–14, Jan. 2024, doi: 10.67228/30715628/IJMIET-2024PI9Y4N.

  • Abstract

    Agentic Artificial Intelligence (Agentic AI) represents the next generation of intelligent systems capable of autonomous sensing, reasoning, planning, and action with minimal human intervention. Unlike traditional AI, Agentic AI integrates large language models, reinforcement learning, multi-agent systems, planning mechanisms, orchestration layers, and memory modules to enable adaptive and goal-oriented decision-making. This paper explores Agentic AI architectures for autonomous business applications, highlighting their role in finance, healthcare, supply chain, enterprise resource planning, customer relationship management, and industrial operations. A layered architecture comprising perception, reasoning, orchestration, and execution layers is proposed to support autonomous analysis, strategic planning, and optimized action execution. The framework also incorporates memory, monitoring, and governance modules to enhance transparency, reliability, and explainability. Experimental evaluation demonstrates that the proposed architecture improves workflow automation, decision accuracy, operational efficiency, resource utilization, and response time while reducing manual intervention. The findings indicate that Agentic AI provides a scalable and robust foundation for future autonomous enterprise systems. The study also discusses key challenges, including explainability, governance, ethics, and trust, emphasizing their importance for successful enterprise adoption. Overall, Agentic AI architectures offer significant potential to accelerate intelligent automation and drive the next generation of business transformation.

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