People with diabetes often show symptoms heavily associated with depression, but these symptoms are often caused by the burden of daily diabetes management. The negative feelings caused by this burden are defined as "diabetes burnout''. Some of these negative feelings are caused by social issues. People with diabetes often resort to online sources to find out how to deal with these social issues since health care providers do not focus on this side of diabetes. Furthermore, the social stigma surrounding treatment for mental problems stops people with diabetes from going to a psychologist. In this project, a conversational agent is designed, implemented and evaluated to investigate whether it is capable of reducing social diabetes distress. The agent was designed to give personalized tips based on a social issue the person with diabetes shares. A longitudinal experiment was done over three sessions to evaluate the agent. The results show that the agent is able to reduce the diabetes distress more than a plain textual delivery of tips. The successful application shows the value of conversational agents and provide a basis to deploy such conversational agents in the e-mental health domain. The design we created can be used in future work, where a further personalized approach and a tool measuring the personalization should be investigated in order to better understand why the conversational agent is able to reduce the diabetes distress.