Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs
Apple's Glyph: multi-agent system solves the data catalog problem at scale—automating column description and governance tagging that humans can't keep up with.
Why it matters
Enterprise data stewardship at scale requires agentic orchestration, not just models. Glyph shows how coupled LLM agents solving interdependent problems (description + classification) can reduce documentation debt and unblock compliance and discovery at companies drowning in undocumented data.
The key facts
12 to knowProduction system from Apple ML Research
Two-agent architecture: Descriptor (grounds in pipeline source code) + Classifier (sensitivity/ontology tagging)
Agents orchestrated as stateful graphs (multi-turn reasoning, not single-shot inference)
Problem: enterprise data lakes accumulate faster than human documentation can scale
Use cases: data discovery, access control, regulatory compliance
Date: September 2026
System name: Glyph
Problem: columns lack descriptions and governance labels faster than human stewards can maintain
Architecture: coupled LLM agents orchestrated as stateful graphs
Key innovation: Descriptor agent grounds generation in pipeline source code retrieved on demand
Use case: enterprise data catalog automation (data discovery, access control, compliance)
Source: Apple Machine Learning Research (production-grade publication venue)
Go to the source
Apple Machine Learningmachinelearning.apple.com
Publisher excerpt: Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production…