Distributed Multi-Agent Systems (DMAS) such as supply chains functioning in highly dynamic environments need to achieve maximum overall utility during operation. The utility from maintaining performance is an important component of their survivability. This utility is often met by identifying trade-offs between quality of service and performance. To adoptively choose the operational settings for better utility, we propose an autonomous and scalable queueing theory based methodology to control the performance of a hierarchical network of distributed agents. By formulating the MAS as an open queueing network with multiple classes of traffic we evaluate the performance and subsequently the utility, from which we identify the control alternative for a localized, multi-tier zone. When the problem scales, another larger queueing network could be composed using zones as bu0ilding-blocks. This method advocates the systematic specification of the DMAS's attributes to aid real-time translation of the DMAS into a queueing network. We prototype our framework in Cougaar and verify our results.