Applied AI often fails at the millisecond-to-minute window where people and systems meet; where timing, transparency, and traceability govern user risk and organizational trust. This article develops a conceptual framework for interaction micro-policies including bounded responsivity (response-time envelopes), verifiable disclosure (user-checkable limits and reliability cues), and audited recovery (tamper-evident incident traces), as microfoundations of responsible AI innovation. Drawing on a synthesis of public incidents, human factors and risk-communication scholarship, design-science principles, and governance standards, the framework specifies constructs, boundary conditions and testable propositions linking these controls to safety, adoption, and auditability. Impact and contributions include: (a) operational definitions and a measurement map suitable for field studies, (b) a procurement checklist and domain profiles for practitioners, and (c) a research agenda for applied psychology focused on cognitive trust, escalation dynamics, and recovery learning. Reframing trustworthy AI as measurable interface constraints enables cumulative evidence building and actionable design across domains.