I’ve been experimenting with letting LLMs generate and then continuously modify small business applications (CRUD, dashboards, workflows). The first generation usually works — the problems start on the second or third iteration.
Some recurring failure modes I keep seeing: • schema drift that silently breaks dashboards • metrics changing meaning across iterations • UI components querying data in incompatible ways • AI fixing something locally while violating global invariants
What’s striking is that most AI app builders treat generation as a one-shot problem, while real applications are long-lived systems that need to evolve safely.
The direction I’m exploring is treating the application as a runtime model rather than generated code: • the app is defined by a structured, versioned JSON/DSL (entities, relationships, metrics, workflows) • every AI-proposed change is validated by the backend before execution • UI components bind to semantic concepts (metrics, datasets), not raw queries • AI proposes structure; the runtime enforces consistency
Conceptually this feels closer to how Kubernetes treats infrastructure, or how semantic layers work in analytics — but applied to full applications rather than reporting.
I’m curious: • Has anyone here explored similar patterns? • Are there established approaches to controlling AI-driven schema evolution? • Do you think semantic layers belong inside the application runtime, or should they remain analytics-only?
Not pitching anything — genuinely trying to understand how others are approaching AI + long-lived application state.
Thanks.
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