The Evolution and Future of Microservices Architecture with AI-Driven Enhancements

The Evolution and Future of Microservices Architecture with AI-Driven Enhancements

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2025 by IJRES Journal
Volume-12 Issue-1
Year of Publication : 2025
Authors : Jill Willard, James Hutson
DOI : 10.14445/23497157/IJRES-V12I1P103

How to Cite?

Jill Willard, James Hutson, "The Evolution and Future of Microservices Architecture with AI-Driven Enhancements ," International Journal of Recent Engineering Science, vol. 12, no. 1, pp. 16-22, 2025. Crossref, https://doi.org/10.14445/23497157/IJRES-V12I1P103

Abstract
Microservices architecture has revolutionized software development by enabling the decomposition of monolithic applications into smaller, more manageable services. While this shift has reduced risks and enhanced system resiliency, the increasing complexity of managing numerous microservices presents new challenges. As Artificial Intelligence (AI) continues to evolve, there is a growing need to explore how autonomous AI agents can optimize microservices architectures, particularly in terms of communication and workflow orchestration. The purpose of this study is to investigate how AI agents can autonomously interact and manage microservices, reducing human intervention and enhancing system efficiency. The key research question guiding this investigation relates to how autonomous AI agents can optimize communication and coordination between microservices to minimize complexity and increase system scalability. Addressing this question is significant because AI agents have the potential to handle routine management tasks, such as load balancing, resource allocation, and service monitoring. This could drastically reduce operational complexities and allow developers to focus on more innovative and strategic functions. The results of this study could pave the way for a new era of AI-augmented microservices, leading to more resilient, scalable, and efficient systems that operate with minimal human intervention.

Keywords
Microservices architecture, Artificial Intelligence, AI agents, System efficiency, Complexity management.

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