AI-Driven Integration Tools for Mitigating API Performance Challenges: Enhancing Business Agility in the Digital Era

In today's digital landscape, businesses increasingly rely on distributed architectures and API-driven integrations to maintain competitive agility. However, performance bottlenecks and optimization challenges in API interactions can lead to operational inefficiencies, degraded customer experience, and increased costs. The implementation of AI-driven frameworks leverages advanced integration tools powered by machine learning to proactively monitor, diagnose, and optimize API performance. By incorporating real-time analytics and predictive modeling, the solution not only detects anomalies and performance degradation but also automates remediation processes, thereby enhancing system reliability and scalability. Through intelligent monitoring and automated optimization, organizations can achieve substantial improvements in response times and resource utilization, ultimately driving better business outcomes and operational excellence in modern digital ecosystems.

Authors

Publication Details

Journal
European Journal of Computer Science and Information Technology
Published
2025-04-15
DOI
https://doi.org/10.37745/ejcsit.2013/vol13n1696106
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
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article

AI-Driven Integration Tools for Mitigating API Performance Challenges: Enhancing Business Agility in the Digital Era

Gouthami Kathala
European Journal of Computer Science and Information Technology
Software System Performance and Reliability
article

AI-Driven Integration Tools for Mitigating API Performance Challenges: Enhancing Business Agility in the Digital Era

Gouthami Kathala
article en

Abstract

In today's digital landscape, businesses increasingly rely on distributed architectures and API-driven integrations to maintain competitive agility. However, performance bottlenecks and optimization challenges in API interactions can lead to operational inefficiencies, degraded customer experience, and increased costs. The implementation of AI-driven frameworks leverages advanced integration tools powered by machine learning to proactively monitor, diagnose, and optimize API performance. By incorporating real-time analytics and predictive modeling, the solution not only detects anomalies and performance degradation but also automates remediation processes, thereby enhancing system reliability and scalability. Through intelligent monitoring and automated optimization, organizations can achieve substantial improvements in response times and resource utilization, ultimately driving better business outcomes and operational excellence in modern digital ecosystems.

European Journal of Computer Science and Information TechnologyVol. 13(16)
Openalex Percentile: Top 90%
Software System Performance and Reliability
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