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Endigest AI Core Summary
This article explores why agentic workflows require near-100% accuracy and how comprehensive database context engineering achieves this.
•Accuracy compounds exponentially across steps: a 90% accurate agent achieves only 59% success over five steps, making such workflows unviable in business-critical applications
•Near-perfect accuracy is essential in customer-facing applications to maintain trust and meet legal, safety, and compliance requirements
•QueryData and similar tools solve this through comprehensive database context with three pillars: Schema Ontology, Query Blueprints, and Value Searches
•Schema Ontology provides natural language descriptions of tables and columns to guide LLMs toward correct query translation
•Query Blueprints enable fine control of SQL generation while Facets and Value Searches handle ambiguities through vector search capabilities
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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