Principles of Designing Self-Learning Data Flow Management Systems in a Business Environment

The article examines the principles of designing self-learning data flow management systems in a business environment. The need to move from static configuration of stream processing infrastructure to adaptive loops based on observability, formalized system state representation, validation of control decisions, and controlled execution of actions is substantiated. Special attention is paid to reinforcement learning as a mechanism for sequential tuning of data processing parameters, taking into account throughput, the volume of unprocessed events, downstream service latency, resource utilization, data freshness, and SLA/SLO compliance. The article also examines the architectural components of such systems, the logic of the reward function, production environment constraints, and application areas in corporate IT infrastructures.