Serverless architectures for agentic AI deployment

This paper presents directions on improving scalabilities, costs, and flexibility in serverless architectures incorporating agentic AI deployment. Using event-driven and a pay-as-you-go model, Serverless computing is shown to be an optimal way to deploy agentic AI systems due to their need for flexibility. The research objectives include the assessment of the possibilities for serverless platforms, the assessment of the effectiveness of its case applications, and the development of a solid methodology for its application in real life. The methodology uses case studies, comparative analysis, and evaluation metrics to determine the benefits of serverless computing to AI workloads. The main findings emphasize latency optimization, cost-effectiveness, and flexibility of operations. These insights are mostly general for businesses, developers, and cloud providers interested in AI effectiveness and deployment. Consequently, this research finds that linking serverless architectures to agentic AI endorses innovation possibilities in deploying AI.