Text-to-SQL agents often fail in production because they rely solely on database schemas, which lack crucial business context. These schemas do not encode semantic meaning, such as metric definitions, canonical join paths, or business glossary terms. To address this, the Operational Knowledge Framework (OKF) proposes using a knowledge graph alongside the schema. This graph stores metric definitions, entity relationships, and business glossary information, enabling agents to query it before generating SQL, thus producing more accurate results. AI
IMPACT Highlights the need for richer context beyond database schemas to improve the accuracy and reliability of text-to-SQL agents in production environments.
RANK_REASON Discusses a technical limitation and proposed solution for text-to-SQL agents.
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