While affective computing has established itself as a cornerstone of human-centered AI, what emerges is a subtle but consequential epistemic gap, i.e., a mismatch between a system’s affective resonance and its causal intelligibility: when AI makes decisions that feel right emotionally but are opaque in reasoning or uncontrollable in behavior, users lose the agentic thread that ties them to causal outcomes. This gap highlights a fundamental dimension of human experience that remains underrepresented in AI system design, the so-called sense of agency (SoA), which is the feeling of being in control of one’s own actions and their causal outcomes. In this paper, we argue that computing for affect and aligning with SoA are methodologically and functionally distinct, even if interrelated. While the former emphasizes affect recognition and feedback, the latter shifts the focus to the preservation and enhancement of user’s SoA during interactions. We build on recent works in the neurocognitive and behavioral sciences, as well as human-centered AI, to argue for agency alignment, i.e., the development of AI systems that preserve and reinforce the user’s sense of self and agency. Thus, we advocate for the AI community to treat SoA not as a soft design issue, but as a computational problem in its own right that should not be rejected as it can be central to trust and accountability in intelligent systems.