Multi-tenant AI platforms must enforce tenant boundaries across APIs, workflows, tools, model calls, storage, logs, and observability. Tenant isolation is a first-class architectural concern, not a database filter.
Multi-agent workflows are useful for design-generation systems, but agents alone do not guarantee good outputs. Production design workflows need quality gates and structured orchestration.
A model-agnostic architecture allows teams to test and switch providers without rewriting product workflows. This essay explains the components needed for practical model flexibility.
AI design applications depend heavily on model output quality. If the architecture is tied to one provider, teams cannot quickly experiment when quality is poor. Model lock-in is a product and release risk.