Exploring dbt and Google with AI agents

AI agents operating within dbt projects represent a meaningful shift in how we think about transformation logic governance. Instead of treating AI as a black box that generates SQL, we're now looking at agents that understand dbt semantics—lineage, testing, documentation—and can reason about data quality before and after changes. This changes our operational posture significantly. Teams moving toward this pattern need to think about LLM hallucination risk in transformation code, which means stronger contract testing and probably more restrictive agent scoping than we currently use with human developers. The broader trend here is declarative infrastructure meeting autonomous reasoning: dbt already codified transformation intent, now we're automating the execution layer while maintaining auditability. My practical takeaway is this—don't jump to full agent autonomy yet. Start by having agents generate dbt test definitions and documentation improvements, where errors are caught by CI before reaching production. That gives you signal on whether agents understand your specific data contracts.