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We consider a network of service-providing agents, where different agents have different capabilities, availability, and cost to solve problems. These characteristics are particularly important in practice for semi-automated call centers which provide quality customer service in real time. We have developed SANet, a service agent network for call center automation, to serve as an experimental testbed for our research. SANet can select appropriate agents to provide better solutions for customer problems according to the changing capabilities and availability of service agents in the network. It can also add or delete appropriate agents to balance problem-solving quality, efficiency, and cost according to the number and types of incoming customer problems. On this network, each service agent can be a human service agent, an automated software service agent, or a combination of the two. This paper describes the architecture, a problem scheduling algorithm and an agent assignment algorithm on the SANet. We highlight an application in which we apply SANet to a call-center scheduling problem for a cable-TV company. Finally, we show the efficiency and adaptability of our system via experimental results and discuss related works.