Frontier AI models crumble when dropped into enterprise relational databases, according to an evaluation by Sanjay Mishra, Divya Chukkapalli, and Ganesh R. Naik. Benchmark testing across a 1,000-pair enterprise verification suite revealed that GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Flash managed just 52.9%, 52.8%, and 52.1% execution match accuracy, respectively. The researchers attribute this failure to schema-graph scaling: real-world production catalogs packed with hundreds of normalized tables, duplicate column names across namespaces, and missing explicit foreign keys completely overwhelm standard LLM attention budgets.
To bridge the gap between raw text-to-SQL generation and data integrity, the team introduced DRL (Deterministic Relational Middleware Layer). The framework establishes a deterministic boundary between conversational agents and transactional SQL backends by combining dynamic context pruning, offline relational AST (Abstract Syntax Tree) typing, and hard execution safeguards like EXPLAIN plan gating and explicit NULL guards. In PostgreSQL benchmarks, DRL's dynamic router slashed prompt context bloat by 92% compared to naive full-catalog ingestion, clocking a p95 pruning latency of 0.58 ms and an overall middleware overhead of just 4.6 ms outside model inference.
Treating enterprise text-to-SQL as an LLM prompting problem is a dead end. Semantic parsers cannot guess implicit catalog relationships or guarantee query execution safety on their own. For technical leaders connecting autonomous agents directly to production OLTP databases, deterministic validation layers represent the only viable path to eliminating data corruption risks without resorting to endless manual query rewriting.