Engram and Harvey trained a legal agent that cut query costs and improved accuracy

Engram · 2 weeks ago · 40

Engram and Harvey trained a legal AI agent that combines parametric knowledge, text notes, and search to handle law firm queries. The agent achieved $0.13 per query at 30% accuracy versus $1.32 per query at 25% for Claude Opus 4.8 on a synthetic 100M-token legal firm dataset. The trained agent learns to perform targeted searches instead of exhaustive document reads, reducing inference costs by an order of magnitude while improving accuracy.