This paper presents an application of reinforcement Q-learning in solving the problem of an automatic call transfer. Given outcomes are the results of simulations; a potential problem has been detected in a telecommunication call center in which a particular association of agents is applied to serve large number of the incoming calls. The main idea presented here is the adaptation of the current call transfer toward an optimal agent. The search for an optimal agent is based on its current and previous activity. The simulations show that implementation of the knowledge about the agent behavior can, in particular situations, significantly accelerate the service system.