Architecting Advanced Generative AI for Cloud-Native Enterprise Intelligence with Autonomous Workflow Optimization Frameworks

Tsungai Tsambatare; Tsungai Tsambatare

doi:10.15662/ijetemr.2025.1006008

Architecting Advanced Generative AI for Cloud-Native Enterprise Intelligence with Autonomous Workflow Optimization Frameworks

Tsungai Tsambatare

American University, Washington, United States

Abstract

Modern enterprises face immense operational complexity when coordinating distributed microservices, multi-cloud platforms, and dynamic business processes. Traditional rule-based automation fails to adapt flexibly to real-time market fluctuations, unexpected system failures, and complex multi-modal unstructured data. This paper presents an architectural framework for integrating Advanced Generative AI (GenAI) into cloud-native enterprise intelligence platforms, specifically optimized for autonomous workflow execution. By coupling Large Language Model (LLM) reasoning with multi-agent orchestration paradigms, the proposed architecture shifts enterprise operations from instruction-driven execution to goal-directed, intent-based autonomy. Our framework features a decentralized, event-driven multi-agent mesh running on Kubernetes, incorporating Retrieval-Augmented Generation (RAG) over corporate knowledge graphs, tool-augmented execution loops, and real-time process mining. To maintain operational stability and regulatory compliance, we introduce a continuous human-in-the-loop (HITL) guardrail system backed by dynamic probabilistic validation. Experimental evaluations on enterprise workloads demonstrate that this architecture reduces end-to-end business process execution times by up to 64%, lowers human manual intervention rates by 72% for semi-structured tasks, and improves system self-healing recovery times during operational incidents. This framework offers a clear path toward scalable, self-optimizing, and secure enterprise AI operations.

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2025.1006008
Pages	18949-18956
Published	November 11, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Tsungai Tsambatare (%2025). Architecting Advanced Generative AI for Cloud-Native Enterprise Intelligence with Autonomous Workflow Optimization Frameworks. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 6 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 18949-18956. https://doi.org/10.15662/ijetemr.2025.1006008

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