Generative AI Architectures for Real-Time Transaction Processing
Keywords:
Generative Artificial Intelligence, GenAI Enabled DevOps, Real Time Transaction Processing, Financial Services Pipelines, Black Swan Event Handling, Enterprise Architecture Theory, Cloud Native Architectures, Transaction Pipeline Scalability, DevOps Automation Practices, Real Time Data Processing, Operational Resilience Engineering, Non Functional Requirements Analysis, Intelligent Pipeline Monitoring, Adaptive System Design, Financial Infrastructure Modernization, Event Driven Architectures, High Volume Transaction Systems, AI Assisted Software Operations, Architectural Patterns And Trade Offs, Enterprise Grade Transaction Systems.Abstract
Generative AI (GenAI) is opening new capabilities in software development and operations, reinforcing the broader shift toward DevOps principles and practices. At the same time, organizations across industries are under growing pressure to process data in real time. In financial services, this pressure is most acute in transaction pipelines, which must absorb volume spikes during Black Swan events despite comparatively modest average loads. GenAI-enabled DevOps offers promising ways to accelerate the design, implementation, and monitoring of such pipelines, yet its adoption in this context surfaces unresolved tensions. This paper reviews the existing literature, identifies gaps, draws on Generalized Enterprise Architecture Theory, and formulates research questions whose resolution can produce solutions that reconcile GenAI-enabled DevOps principles with the demands of real-time transaction processing. Toward that end, the paper lays the groundwork by specifying the functional and nonfunctional requirements of such pipelines, surveying GenAI-driven DevOps concepts, architectures, and operational paradigms relevant to financial systems, and outlining suitable design patterns along with their associated challenges and trade-offs.
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