From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents

Abstract--Amid the rapid rise of agentic dialogue models, realistic user-simulator studies are essential for tuning effective conversation strategies. This work investigates a sales-oriented agent that adapts its dialogue based on user profiles spanning age, gender, and occupation. While age and gender influence overall performance, occupation produces the most pronounced differences in conversational intent. Leveraging this insight, we introduce a lightweight, occupation-conditioned strategy that guides the agent to prioritize intents aligned with user preferences, resulting in shorter and more successful dialogues. Our findings highlight the importance of rich simulator profiles and demonstrate how simple persona-informed strategies can enhance the effectiveness of sales-oriented dialogue systems. With the ongoing evolution of Agentic AI, researchers have begun to explore its application across diverse domains. Among these, dialogue systems designed for business recommendation tasks have attracted significant attention.