The Courtroom is a State Machine: Architecting Agentic Memory for Litigators

Can a generative AI agent truly navigate the intricate, ever-shifting facts of a complex judicial trial? While standard language models fail to track how evidence chronologically mutates over time, I propose architecting a stateful research engine backed by persistent graph memory to map the objective truth. By coupling this digital corkboard with a rigorous automated evaluation pipeline, we can finally turn raw courtroom transcripts into a reliable, trackable state machine for litigators.