A Semantic-Layer-Mediated Agent for Natural Language to SQL over Heterogeneous Enterprise Databases
arXiv:2606.31041v1 Announce Type: new Abstract: Natural language-to-SQL (NL2SQL) over real-world enterprise databases remains significantly more challenging than on academic benchmarks. Enterprise schemas often contain hundreds of physical tables with cryptic column names, heterogeneous SQL dialects, and complex analytical workloads requiring nested aggregations, temporal reasoning, and multi-table joins. We present a semantic-layer-mediated NL2SQL agent that decouples semantic intent from physical SQL execution. Rather than generating SQL directly over raw schemas, the agent reasons over a cu
The proliferation of complex enterprise data infrastructures and the rapid advancements in large language models make the challenge and potential solution of NL2SQL agents increasingly relevant.
This development addresses a critical bottleneck in enterprise data access, enabling non-technical users to query complex databases, which can significantly accelerate data-driven decision-making and innovation within organizations.
The reliance on specialized data analysts and engineers for routine data querying will diminish, as sophisticated AI agents take on the role of translating natural language requests into efficient, accurate SQL.
- · Enterprise software companies
- · Data analytics platforms
- · Large enterprises
- · AI developers
- · Entry-level data analysts
- · SQL-as-a-service providers reliant on manual work
Increased accessibility of enterprise data for business users, leading to faster insights and potentially improved operational efficiency.
A shift in demand for data professionals towards more complex modeling, governance, and AI-driven solution development, rather than routine querying.
Enhanced competition among enterprises that can more effectively leverage their internal data assets, potentially creating new market leaders and disrupting traditional industries.
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Read at arXiv cs.CL