Multi-tenant Model-as-a-Service (MaaS) LLM serving must maintain stringent quality of service (QoS) despite heterogeneous requests competing for constrained GPU resources. In practice, MaaS workloads exhibit non-stationarity across multiple time scales, including request bursts, request-composition drift, and persistent workload shifts. Because workloads change across multiple time scales, existing request schedulers often rely on a single fixed policy (e.g., First-Come-First-Served, FCFS) that remains unchanged at runtime, which can lead to unstable QoS. We propose H-MAS, a hierarchical multi-agent scheduler that operates in a layered closed loop: a perception/prediction layer infers lightweight request attributes and cost signals; a feedback layer summarizes runtime metrics into short- and long-horizon QoS states; a hierarchical control layer updates the active scheduling policy over longer horizons and tunes execution parameters over shorter horizons; and an execution layer applies these decisions during inference. Experiments with load scaling and Azure-trace replays show that H-MAS achieves 1.2×–3.0× higher Goodput than SGLang and vLLM, and maintains more stable QoS under workload drift, diverse request lengths and heterogeneous SLO targets.

Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictable disruptions. These constraints undermine their ability to dynamically adjust long-term strategies and inter-agent collaboration. To address these challenges, we propose DeMAC, a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. DeMAC uses a dynamically updated directed acyclic graph (DAG) and a Manager-Player Dual-Feedback mechanism to align strategic and operational decisions. Moreover, DeMAC enables agents to maintain collaboration and dynamically adapt to changing environmental conditions, outperforming traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. Experimental results highlight DeMAC’s ability to tackle complex coordination tasks, demonstrating its potential to advance LLM-based MAS in dynamic, complex task dependency environments.