While interest in AI conversational agents has grown rapidly in recent years, creating agents capable of holding recipient-specific conversations remains a challenge. In other words, an automated agent should be able to engage in recipient design or tailoring the form of its utterances and selection of utterances based on the user's knowledge, or assumptions about what the user knows. Without recipient design, the agent formulates its utterances the same way for every user. While an agent might still fall back on conversational repair practices, such as paraphrase or definition requests, recipient design can minimize the necessity for repair in the first place. In this work, we leverage recipient design methods from natural conversation, as identified in the field of Conversation Analysis (CA) and implement three of them (self-reports of knowledge, prior difficulty in understanding, and prior exposure to a reference) as part of our conversational agent, the Alma Assistant.