An Adaptive AI Multi-Agent Model for Optimizing Real-Time Data Streaming and System Resilience

Abstract

Real-time data-streaming environments increasingly require adaptive decision mechanisms capable of coordinating heterogeneous computational resources while maintaining throughput, resilience, and service continuity under dynamic workloads. This paper proposes a conceptual adaptive artificial intelligence (AI) multi-agent model for optimizing real-time data streaming and system resilience. The proposed model combines autonomous agents for stream monitoring, workload allocation, resource coordination, anomaly response, and resilience management within a decentralized decision architecture. Its theoretical foundation is informed by research on distributed allocation, fairness, efficiency, optimization, and computational complexity in multi-agent decision environments. In particular, studies of fair and efficient allocation provide useful principles for balancing competing resource demands, while work on Nash social welfare and allocation algorithms demonstrates the value of optimization objectives that consider collective system utility. The proposed architecture extends these principles from indivisible-resource allocation toward dynamic streaming-resource management. The methodology defines agent roles, state representation, utility functions, adaptive allocation policies, coordination mechanisms, resilience procedures, and evaluation criteria. Analytical findings indicate that adaptive multi-agent coordination can improve resource utilization, reduce the impact of localized failures, and support scalable stream processing when compared conceptually with rigid centralized allocation. The model is particularly relevant to event-streaming environments in which workload intensity, resource availability, and service conditions change continuously. The paper further identifies limitations concerning coordination overhead, convergence, observability, and the absence of empirical benchmarking in the present conceptual study. The framework therefore provides a research foundation for implementing resilient AI-driven streaming systems and for future experimental validation.