Separating Facts from Interpretations in Agent Knowledge Graphs

This content proposes separating facts from interpretations in agent knowledge graphs used with LLM systems to address issues with scaling, governance, and evolution. This approach, implemented with two distinct physical tables, significantly improved output quality (+375%) and work success rates (65.3% to 99.1%) in a running agent society.