Graph-Aware Scheduling for Multi-Agent Workflows in Edge-Cloud Environments

Multi-agent workflows have emerged as an important execution pattern for intelligent applications, where specialized agents collaborate through dependent stages such as planning, retrieval, execution, and verification. When such workflows are deployed over edge-cloud infrastructures, scheduling becomes challenging because task dependencies, heterogeneous resource conditions, and topology-dependent communication overhead must be considered jointly. We study the problem of scheduling multi-agent workflows in edge-cloud environments and propose a graph-aware scheduling method that models workflow execution as a task graph and the underlying infrastructure as a resource graph. The method combines structure-aware task and resource representations, communication-sensitive assignment scoring, and online resource-state updates to improve task placement quality. Experiments across different workflow complexities, system scales, and dynamic operating conditions show that the proposed method achieves a favorable balance among latency, communication overhead, and execution cost, particularly in communication-sensitive and medium-to-large-scale settings. These results suggest that jointly modeling workflow structure and resource topology can improve scheduling quality for multi-agent workflow execution in heterogeneous edge-cloud environments.