The Integration of AI Agents in Service Assurance

The integration of AI agents in network management represents a transformative advancement in telecommunications infrastructure operations. Through a multi-layered architecture incorporating edge computing, cloud analytics, and hybrid system integration, the implemented solution delivers quantifiable improvements in operational efficiency, resource optimization, and cost reduction. The framework encompasses comprehensive data processing capabilities through distributed computing layers while maintaining robust security protocols. The implementation demonstrates significant enhancements in anomaly detection, fault management, and predictive maintenance across multiple enterprise deployments. By leveraging advanced machine learning models and sophisticated automation mechanisms, the system achieves marked improvements in network optimization, incident resolution times, and resource utilization, establishing a foundation for next-generation network management solutions.

Authors

Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-03-23
DOI
https://doi.org/10.32628/cseit25112538
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

The Integration of AI Agents in Service Assurance

Venkatesh Cumbakonam Gokulraju
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Business Process Modeling and Analysis
article

The Integration of AI Agents in Service Assurance

Venkatesh Cumbakonam Gokulraju
article en

Abstract

The integration of AI agents in network management represents a transformative advancement in telecommunications infrastructure operations. Through a multi-layered architecture incorporating edge computing, cloud analytics, and hybrid system integration, the implemented solution delivers quantifiable improvements in operational efficiency, resource optimization, and cost reduction. The framework encompasses comprehensive data processing capabilities through distributed computing layers while maintaining robust security protocols. The implementation demonstrates significant enhancements in anomaly detection, fault management, and predictive maintenance across multiple enterprise deployments. By leveraging advanced machine learning models and sophisticated automation mechanisms, the system achieves marked improvements in network optimization, incident resolution times, and resource utilization, establishing a foundation for next-generation network management solutions.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(2)
Openalex Percentile: Top 92%
Business Process Modeling and Analysis
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