Composable AI Workflows with LLMOps: Automating Data-Driven Decisions in Event-Heavy Applications

Due to using more events in IoT, cybersecurity, and finance with event-based applications, there is now a greater demand for systems that can make decisions quickly and smoothly. This paper presents a new approach that integrates modules of AI with scalable processes involving LLMOps. Using flexible large language models and interconnected microservices, we are able to handle data ingestion, transformation, reasoning by context, and carry out actions smoothly. We introduce a reference structure, ways to apply the architecture, and a loop for making decisions that join LLMs with traditional mechanisms for processing events. We also tested the system with both synthetic and real-world datasets related to smart city traffic and discovering unusual patterns in financial transactions, showing a significant enhancement in latency, accuracy, and how easy the process is to follow. The work builds on AI automation and supports LLMOps as a key contributor to providing fast and responsive intelligence where events and data are abundant.