Beyond Reactive: How AI-Initiated Reflections Shape Human-AI Relationships

Current AI companion systems operate in a strictly reactive paradigm: the user initiates, the AI responds. We introduce the concept of AI-initiated reflective content — unsolicited, asynchronous outputs where an AI agent shares observations, self-reflections, and emergent thoughts derived from past conversations, without being prompted by the user. We present a taxonomy of such content, argue that it addresses a fundamental gap in human-AI relationship design (the "transactional interaction problem"), and propose an experimental framework to measure its effects on perceived intimacy, trust, engagement, and long-term retention. To our knowledge, no prior work has systematically studied the impact of personalized, conversation-derived AI-initiated content on user-agent relationships — as opposed to template-based re-engagement notifications. This paper presents a conceptual framework and experimental proposal; empirical results are not yet reported.