Paralleling rapid advancement in the telecommunication network expansion is necessary for advanced network traffic management surveillance. The increasing number and variety of services being offered by networks have emphasized the demand for optimized load management strategies. The paper deals with regulation of a mobile agent moving toward service processing resource in the part of the agent network. We have constructed the agent architecture for the control of service processing load. The goals of the controlling system were both to protect the processing load and to predict the arrival rate of client?s requests. Self-adaptive property is implemented by reinforcement Q-learning. The analysis is based on experimentation through simulations.