Low-code platforms accelerate development of mission-critical applications, enabling users with less technical backgrounds to become proficient developers. AI-powered multi-agent systems further boost this experience. Large language models (LLMs) excel at understanding user intent, but alone cannot guarantee that design rules are followed - formal methods can provide such guarantees. We propose a hybrid approach that leverages both LLMs and a maximum satisfiability solver to generate layouts that comply with UX guidelines (e.g., widget ordering and grouping rules) while respecting user intent. We evaluate the approach in production against an LLM-only baseline.