Modern software-intensive systems must address a wide range of nonfunctional requirements (NFRs) - such as security, compliance, and maintainability - that are critical for the long-term success of the system. With the rise of large-language-model-based agents, software engineering is entering an 'agentic' era where AI components are not only tools but collaborators in development processes. However, leveraging these agents introduces dual challenges: ensuring that AI components themselves meet quality standards (e.g., compliance, security, maintainability), and harnessing AI effectively to support systemlevel NFR assurance. Our perspective explicitly spans both SE4AI, where AI components such as agents are engineered and subjected to quality assurance and AI4SE, where AI agents support the engineering of software-intensive systems. While these are conceptually distinct, our model addresses both in a unified way. This position paper introduces a conceptual, domain-agnostic three-layer model - comprising Data, Agent, and Perspective layers - for systematically embedding AI agents into NFR assurance across the software lifecycle. The model explicitly captures two complementary viewpoints: Quality for AI (ensuring AI agents are trustworthy and maintainable) and AI for Quality (using agents to support system NFRs). Through illustrative examples in compliance, security, and maintainability, the paper demonstrates how this model can guide researchers and practitioners in designing agent-based approaches to software quality. We argue that this model not only clarifies the dual roles of AI in software engineering but also provides a foundation for responsible, scalable, and effective integration of AI into NFR assurance.