Conversational agents stand to play an important role in supporting behavior change and well-being in many domains. With users able to interact with conversational agents through both text and voice, understanding how designing for these channels supports behavior change is important. To begin answering this question, we designed a conversational agent for the workplace that supports workers’ activity-journaling and self-learning through reflection. Our agent, named Robota, combines chat-based communication as a Slack Bot and voice interaction through a personal device using a custom Amazon Alexa Skill. Through a 3-week controlled deployment, we examine how voice-based and chat-based interaction affect workers’ reflection and support self-learning. We demonstrate that, while many current technical limitations exist, adding dedicated mobile voice interaction separate from the already busy chat modality may further enable users to step back and reflect on their work. We conclude with discussion of the implications of our findings to design of workplace self-tracking systems specifically and to behavior-change systems in general.