A 5-member DWH team. 100+ concurrent requests. 5,000+ active ETLs. We tried the shortcut: let Claude generate SQL and have engineers review it. It failed: code generation without validation just shifts the burden; engineers still had to mentally execute every model. The real answer: full-stack sandbox validation. From Prompt to PR, every AI-generated model clears four layers before a human sees it: SQL compilation, dbt tests (uniqueness, range, relationships), orchestration via auto-generated Airflow DAG, and lineage integrity in OpenMetadata. All four must pass. Failures loop back to the agent, not the engineer. Guardrails are structural: read-only prod ServiceAccount, macro-enforced dev schema isolation, ephemeral Kyuubi compute per run. In production: 45% of new models carry the "ai-generated" tag. You'll leave with: the full-stack sandbox architecture, the four-layer validation blueprint, structural guardrails, and governed promotion via explicit PR reviews.