Most autonomous agents fail in production not from flawed reasoning, but from state corruption and race conditions across systems of record. When multiple agents trigger concurrent tools, eventual consistency breaks down. This session examines the database architecture required for production-grade autonomy: scaling high-throughput feature ingestion during model preparation, and enforcing multi-region ACID consistency alongside hybrid vector-relational retrieval during live inference using Spanner, AlloyDB, and Bigtable.