We spent 11x more tokens on reasoning and got 0% better answers on revenue data ... until we added a knowledge graph. TLDR: our latest research shows that improving data representation increases agent retrieval accuracy more than upgrading the model. We ran 3,100 runs across 8 models answering revenue questions, like deal amounts, contacts, and identifying customer champions. We compared two ways of storing the data: 1. a normal database (SQL) VS. 2. a knowledge graph (relationships pre-mapped) Frontier models hit 8.9% accuracy on the questions using SQL over a relational schema. Cranking Claude Opus 4.8’s reasoning effort from minimum → maximum accuracy did not help. However, swap raw Salesforce data for a knowledge graph built on lakehouses like @databricks, @Snowflake, @googlecloud's Big Query, or @Azure Data Fabric ... and accuracy jumps from 8.9% to 99.9% - even using a 27B open-weight model at 1/20th the cost. This research shows throwing more compute at your agent cannot fix bad data structure. And is proof a revenue-specific knowledge graph is key to making revenue agents work at scale.