🤖 AI Summary
Current AI coding agents, in the absence of explicit architectural design, rapidly make unvetted software architecture decisions through implicit mechanisms. This work identifies five such implicit architecture-shaping mechanisms and introduces, for the first time, the concept of “vibe architecting,” establishing a mapping between natural language prompt characteristics and system architectural requirements. It further distills six prompt-architecture coupling patterns. Integrating large language model prompt engineering, tool-call orchestration, structured output validation, and architectural decision recording, the study empirically demonstrates that subtle differences in prompt phrasing alone can yield substantially divergent system architectures. Building on these findings, the paper proposes corresponding review practices and tooling support to advocate for effective governance of implicit architectural decisions.
📝 Abstract
AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural decisions, yet almost no one reviews them as such. We identify five mechanisms by which agents make implicit architectural choices and propose six prompt-architecture coupling patterns that map natural-language prompt features to the infrastructure they require. The patterns range from contingent couplings (structured output validation) that may weaken as models improve to fundamental ones (tool-call orchestration) that persist regardless of model capability. An illustrative demonstration confirms that prompt wording alone produces structurally different systems for the same task. We term the phenomenon vibe architecting, architecture shaped by prompts rather than deliberate design, and outline review practices, decision records, and tooling to bring these hidden decisions under governance.