Multi-Agent Systems for Autonomous Data Pipeline Optimization in AI Workflows

Abstract

The optimization of data pipelines is critical for enhancing the performance and efficiency of AI workflows, which often involve complex, heterogeneous, and dynamic data processing stages. Traditional approaches to pipeline optimization struggle to adapt autonomously to evolving workloads and system conditions. This paper proposes a novel multi-agent system (MAS) framework that enables autonomous optimization of data pipelines in AI workflows. Each agent is responsible for specific tasks such as data ingestion, transformation, scheduling, and resource management, and they collaborate through adaptive protocols to achieve global optimization objectives. We demonstrate the effectiveness of the proposed framework through extensive experiments, showing significant improvements in pipeline throughput, latency, and resource utilization compared to conventional methods. Our approach highlights the potential of MAS to bring intelligence, flexibility, and scalability to data pipeline management in AI systems.