In social service centers, practitioners engage in conversations with clients with dementia to facilitate their daily activities and provide support when they are distressed. However, the nature of the care demands the practitioner’s active engagement, which becomes difficult to deliver as the number of people who need care expands. Researchers have been investigating the efficacy of developing agents that assume conversational tasks to alleviate this work. To contribute to the future design of agents for caregiving, we collected and analyzed ten conversations between clients with mild dementia and practitioners who provide care. Our analyses of turn-taking dynamics and dialogue acts with 15k utterances uncovered patterns such as noticeable differences in clients’ and practitioners’ conversational dynamics and the prevalence of neutral-toned, question-oriented utterances by practitioners. We then prototyped a large language model-based script that generates responses to client utterances. We found potential approaches and challenges for making its utterance pattern more similar to that of a practitioner.