AI-Driven Multi-Agent Framework for Resilient and Scalable Event Data Streaming

Abstract

The increasing volume, velocity, and heterogeneity of event data require streaming architectures capable of adapting continuously to changing workloads while maintaining low latency, high throughput, and operational resilience. Conventional event-streaming pipelines frequently rely on relatively static resource allocation, routing, and fault-management mechanisms, which can become inefficient when traffic patterns, resource availability, or application priorities change dynamically. This paper proposes an AI-driven multi-agent framework for resilient and scalable event data streaming in which specialized intelligent agents collaboratively perform workload assessment, resource adaptation, fault detection, routing optimization, and streaming control. The theoretical foundation integrates continual-learning principles with distributed agent coordination so that streaming decisions can evolve without requiring complete retraining after every environmental change. Existing continual-learning studies demonstrate the importance of controlling interference, preserving useful knowledge, adapting model parameters, and maintaining task-specific behavior under sequentially changing conditions (Aljundi et al., 2019a; Ahn et al., 2019; Adel et al., 2020). Building on these principles, the proposed framework conceptualizes event streaming as a continuously evolving decision environment rather than a static data-processing problem. The methodology defines cooperating agents for workload monitoring, resource allocation, fault management, and adaptive stream optimization, coordinated through feedback-driven decision cycles. The framework is positioned as an architectural and analytical contribution rather than a claim of experimentally measured performance. Its principal expected outcomes are improved adaptability, reduced disruption during workload changes, better resource utilization, and enhanced resilience under streaming failures. The study further identifies limitations associated with agent coordination overhead, continual-learning instability, computational cost, and the possibility of conflicting local objectives. The resulting framework provides a research foundation for intelligent, adaptive, and scalable event-streaming systems.