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Real-Time AI with EventBridge and Step Functions: Intelligent Orchestration for Business Pipelines

Author(s) Srikanth Jonnakuti
Country United States
Abstract In modern enterprises, the rapid fusion of cloud serverless technologies with artificial intelligence (AI) promises near real-time decision-making capabilities. This paper presents a technical study of intelligent orchestration architectures that combine AWS event-driven services – notably Amazon EventBridge and AWS Step Functions – with custom AI workflows. Our focus is to enable fast, intelligent business decisions in real-time environments while addressing performance, scalability, and reliability challenges. We review related work on serverless computing and AI workflow management, highlighting gaps in existing solutions. We then propose architectural patterns wherein EventBridge event buses trigger Step Functions state machines integrated with AI components (e.g., AWS Lambda, Amazon SageMaker) to form responsive, learning-driven pipelines. We discuss applications such as real-time fraud detection, supply chain optimization, and personalized customer experiences, demonstrating how our approach improves decision latency and adaptability. We analyze challenges including orchestration overhead, cold-start latency, state management, and security constraints, citing empirical findings from recent studies. Finally, we explore future trends like the integration of foundation models and edge computing for composite AI systems. The paper’s contributions aim to guide enterprise and cloud solution architects in designing EB1A-grade intelligent pipelines that leverage event-driven architectures for scalable, real-time AI.
Field Engineering
Published In Volume 6, Issue 1, January 2025
Published On 2025-01-03
Cite This Real-Time AI with EventBridge and Step Functions: Intelligent Orchestration for Business Pipelines - Srikanth Jonnakuti - IJLRP Volume 6, Issue 1, January 2025. DOI 10.70528/IJLRP.v6.i1.1559
DOI https://doi.org/10.70528/IJLRP.v6.i1.1559
Short DOI https://doi.org/g9nhrt
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