Resilient and Scalable Event Streaming Using Intelligent Multi-Agent AI Architectures

Abstract

The increasing dependence on event-driven applications has intensified the need for streaming architectures that can sustain high data rates while remaining resilient to workload fluctuations, failures, and changing computational conditions. Conventional event-streaming systems generally depend on static routing, predefined optimization rules, and centralized control mechanisms, which can become inefficient when streaming workloads exhibit dynamic and heterogeneous behavior. This paper develops a research-oriented conceptual framework for resilient and scalable event streaming using intelligent multi-agent artificial intelligence (AI) architectures. The proposed approach combines multi-agent decision-making with graph-based representation, uncertainty-aware learning, optimization techniques, and adaptive heuristic control. The theoretical foundation is derived exclusively from the provided literature on uncertainty quantification, mixed-integer programming, primal heuristics, machine learning for combinatorial optimization, graph neural networks, and AI-based optimization. The methodology models event-streaming infrastructure as a dynamic optimization environment in which specialized agents cooperate to perform workload prediction, routing, resource allocation, anomaly detection, and recovery decisions. Graph-based representations allow relationships among producers, brokers, consumers, queues, and computational resources to be incorporated into decision processes, while uncertainty-aware mechanisms improve the reliability of learned decisions. The resulting framework indicates that intelligent agents can improve adaptability by distributing decision responsibilities and dynamically selecting optimization strategies according to workload conditions. However, the approach introduces additional computational, coordination, and explainability requirements. The study therefore positions multi-agent AI not as a replacement for established optimization methods, but as an adaptive supervisory layer capable of selecting and coordinating optimization mechanisms for resilient event-streaming environments.