Engram studies legal agents that combine notes, weights, and search

Engram describes an agent trained to study a synthetic law firm’s persistent filesystem before answering legal-work questions. The company says the system combines parametric knowledge, structured text notes, and search, aiming to avoid repeatedly reading the same documents. In experiments with the Calderwood & Harkness workspace, Engram reports lower cost per query and better all-pass performance than the cited Opus 4.8 comparison, while presenting its compute-scaling observations as early results. The post matters because it frames reusable workspace memory as a way to shift some agent work from repeated inference into preparation.