TL;DR: Quality Attributes (often called Non-Functional Requirements) are the true drivers of software architecture. Without defining exactly how a system behaves — using measurable frameworks like Bass-style scenarios — AI coding agents will rapidly veer off the intended architectural path, inevitably leading to costly re-architectures. To govern these AI interactions effectively, you can leverage specific agent skills for quality attributes and functional requirements to ensure your architecture quality.
Early in my career — long before the current wave of AI-assisted development — a product manager handed me a set of system requirements and declared they had “no non-functional requirements.”
My response was straightforward: “Without them, I will build the system I want to build. It will likely follow the easiest path for development, but it won’t be what you actually need.”
That alarmed him — and rightfully so.
In software architecture, capturing efficiency quality attributes — often referred to as non-functional requirements (NFRs) — is arguably the most critical phase of system design. I prefer the term “Quality Attributes.” Architecture is fundamentally about defining the quality of a system. The term “Non-Functional Requirement,” by contrast, focuses on what a system is not, rather than what it is.
The Bass-Style Scenario Framework
Effective Quality Attribute scenarios were well defined by L. Bass, M. Klein, and F. Bachmann, and they have been widely embraced by the architecture community.
An effective quality scenario forces all stakeholders to think about measurable system attributes so the architectural solution can be objectively measured against the requirements.
Why Quality Attributes Matter Even More in the Age of AI
With the rapid rise of agentic development and “vibe coding,” the traditional SDLC process is fundamentally shifting. As Google recently outlined in their piece on the new SDLC, human-to-AI handoffs are becoming the norm.
During these conversations between humans and AI, explicitly captured architectural requirements become hyper-critical. Large Language Models (LLMs) and coding agents will take the path of least resistance if boundaries aren’t clearly defined.
To bridge this gap, the requirement preparation and review phases — whether conducted by a human or an AI agent — must be enhanced. AI agents can be guided to Create, Evaluate, and Fix well-structured quality-attribute scenarios using the Bass-style approach. You can explore concrete implementations of these agent skills for quality attributes and functional requirements on GitHub.
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