Quality and performance improvements¶

Problem¶

As usage scales, the context catalog's enrichment pipeline will face both quality and performance pressure — profiling queries that are too slow for large tables, enrichment rules that produce inaccurate metadata for edge-case schemas, and token-inefficient context formatting that wastes AI agent budget. Without targeted improvements driven by measured user-facing outcomes (query accuracy, agent success rate, profiling latency), optimization efforts will be unfocused and improvements will not translate to visible user benefit.

Quality and performance improvements¶

Problem¶

Context¶

Possible Solutions¶

Plan¶

Implementation Progress¶

Review Feedback¶