Multi-Agent Data Processing Architectures for Adaptive Enterprise Analytics Systems

Abstract

Adaptive and scalable analytics need new design paradigms for corporate data volume, velocity, and heterogeneity. Real-time transformations, dynamic work allocations, and interoperability plague monolithic, centralized data processing systems. This article discusses multi-agent data processing architectures where cooperative AI agents collect, modify, and assess remote business data. Context-aware job allocation, inter-agent interaction, and agent autonomy improve system throughput, robustness, and flexibility. Contributions include developing agent-based interaction protocols, expanding cooperative processes, and adapting resource management to data and analytics. Experimental systems using simulated and real-world business datasets surpass conventional systems in processing, fault tolerance, and latency. Intelligent, self-organizing corporate analytics systems affect data-driven decision-making, operational efficiency, and enterprise-scale AI adoption.