New LLM models are introduced without disrupting workflows because the Knowledge Fabric and agent infrastructure are model-agnostic - they consume structured metadata outputs regardless of which LLM generated them. New models can be tested in parallel for document classification, entity extraction, and schema understanding against a golden dataset of previously processed documents, allowing direct comparison of accuracy, confidence scores, and metadata quality before deployment. Once validated, models can be swapped seamlessly at the Studio, or different agents can be configured to use different models for specific tasks without requiring changes to the Knowledge Fabric schema, governance policies, or data access workflows. Users can build agents that automatically route different document types or data sources to different models based on performance characteristics, cost, or compliance requirements. This architecture ensures continuous model improvement without disrupting ongoing data cataloging, lineage tracking, or governance operations.