16. A system for routing calls comprising: a feature extraction component which extracts features from an utterance of a customer call; a call classification component which predicts a class of a dialogue to be conducted between the customer and an agent, based on the extracted features; a call complexity component which predicts a complexity of the dialogue to be conducted between the customer and the agent, based on the extracted features; a routing component which generates a routing strategy for steering the call to one of a plurality of types of agent with a learned routing model, the routing strategy being based on the predicted class of the dialogue, the predicted complexity of the dialogue, and a cost assigned to each type of agent, a first of the plurality of types of agent being assigned a higher cost than a second of the types of agent, wherein the routing model computes an expectation of success of the dialogue for each of the types of agent, based on the predicted class and complexity of the dialogue, and computes the routing strategy as function of the expectation of success and the assigned cost for each type of agent; an output component which outputs the routing strategy; a feedback component which assesses a success of the dialogue, based on feedback; an update component which updates the routing model, including: computing a difference between the expectation of success, computed by the routing model for the type of agent to which the call has been routed, and the assessed success; and updating the routing model to reduce the difference; and a processor which implements the components.
16. A system for routing calls comprising: a feature extraction component which extracts features from an utterance of a customer call; a call classification component which predicts a class of a dialogue to be conducted between the customer and an agent, based on the extracted features; a call complexity component which predicts a complexity of the dialogue to be conducted between the customer and the agent, based on the extracted features; a routing component which generates a routing strategy for steering the call to one of a plurality of types of agent with a learned routing model, the routing strategy being based on the predicted class of the dialogue, the predicted complexity of the dialogue, and a cost assigned to each type of agent, a first of the plurality of types of agent being assigned a higher cost than a second of the types of agent, wherein the routing model computes an expectation of success of the dialogue for each of the types of agent, based on the predicted class and complexity of the dialogue, and computes the routing strategy as function of the expectation of success and the assigned cost for each type of agent; an output component which outputs the routing strategy; a feedback component which assesses a success of the dialogue, based on feedback; an update component which updates the routing model, including: computing a difference between the expectation of success, computed by the routing model for the type of agent to which the call has been routed, and the assessed success; and updating the routing model to reduce the difference; and a processor which implements the components. 17. The system of claim 16 , further comprising a virtual agent configured for conducting a dialogue with a customer.