Task-Aware Communication for Distributed LLM Agent Systems

Multi-agent systems (MAS) powered by large language models increasingly coordinate reasoning, tool use, and data processing across distributed environments, where task completion time depends largely on the quality of network connectivity. Existing MAS frameworks focus on managing task workflow and resources on the application level, overlooking the opportunities for performance optimization in the underlying network substrate. Building on the emerging path-aware networking paradigm, we propose *FlowAhead*, a framework that leverages task information and network measurements to choose optimal paths for inter-agent data transfers. Our evaluation shows that *FlowAhead* reduces the median task completion time of a three-agent video-reasoning workflow by 34.5% relative to default BGP routing, and by 51.5% without site-access limits, demonstrating a promising path toward improving efficiency for distributed MAS.