Open Access

AI-Driven Automation in Cloud-Based Business Systems: A Practical Implementation Using Microservices Architecture

https://doi.org/10.55640/ijmcsit-v03i05-03

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Serhii Svynarov ¹ ,

⁴ Senior Python Engineer & Cloud Security Researcher, Restart AI LLC Miami, FL

Abstract

Objective: - The study examines how artificial intelligence components, when embedded directly into cloud-based microservices architectures, alter operational performance in enterprise environments. Rather than treating AI as analytics overlay deployed after the fact, the research focuses on configurations in which machine-learning models function as first-class participants in service orchestration, inter-service routing, and domain-specific processing.

Methods. A structured review of peer-reviewed publications and industry reports from 2023 to 2026 was combined with a comparative analysis of three enterprise deployment scenarios covering retail e-commerce, financial services compliance automation, and hybrid-cloud healthcare data processing.

Results. Predictive autoscaling reduced idle resource expenditure by 30-42% relative to static threshold-based policies. Intelligent service-mesh routing lowered peak-period request latency by 18-27%. AI-assisted incident classification cut mean time to resolution by 47%, and the share of incidents requiring manual re-routing fell from 31% to 8%. Anomaly detection embedded at the service level reduced mean time to recovery by more than 60% compared with conventional alerting systems.

Conclusions. The evidence supports a layered integration model in which AI components operate at three distinct levels - infrastructure orchestration, inter-service communication, and business-logic processing - rather than being consolidated into a single intelligent layer. Each level carries distinct requirements regarding model interpretability, update frequency, and fault-tolerance tolerance. Practical guidance is offered for architects and engineering teams, and the study identifies model explainability and cross-vendor data portability as the primary constraints on broader adoption.

Keywords

AI-driven automation, microservices architecture, cloud computing

References

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Moreschini, S., et al. (2023). AI techniques in the microservices life-cycle: A systematic mapping study. arXiv. https://arxiv.org/abs/2305.16092

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Ramareddy, S. K. (2023). Cloud-native microservices for scalable AI-driven business process automation. International Journal on Advanced Computer Theory and Engineering. https://www.researchgate.net/publication/398281417

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Willard, J. (2025). The evolution and future of microservices architecture with AI integration. International Journal of Research in Engineering and Science, 12(1). https://ijresonline.com/archives/ijres-v12i1p103

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AI-Driven Automation in Cloud-Based Business Systems: A Practical Implementation Using Microservices Architecture

Abstract

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