Agentic AI Frameworks for Autonomous Cloud Resource Management and Enterprise Workload Optimization
Abstract
Agentic AI frameworks enable autonomous cloud resource management by continuously analyzing workloads, predicting resource requirements, and dynamically optimizing infrastructure allocation. This framework integrates intelligent agents, machine learning, real-time monitoring, and policy-based automation to improve scalability, performance, and cost efficiency across enterprise cloud environments. By autonomously coordinating provisioning, workload scheduling, resource scaling, and operational decisions, the proposed approach reduces manual intervention and enhances infrastructure resilience. The framework supports adaptive workload optimization while maintaining governance, security, reliability, and efficient utilization of distributed cloud resources.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 10 No. 3 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
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Published |
May 5, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Martin Odersky (%2025). Agentic AI Frameworks for Autonomous Cloud Resource Management and Enterprise Workload Optimization. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 3 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) . https://doi.org/10.15662/f7yx0d49 |
References
No references available for this article