Pradeep

PRANA is an environment for training and evaluating AI agents on clinical administrative workflows. Tasks such as patient triage and transplant management involve long-horizon reasoning, fragmented data sources, and strict regulatory validation. PRANA simulates these workflows through stochastic patient data and multiple scenario trajectories, allowing agents to query records, reconcile missing or stale information, and complete regulatory reporting tasks. The environment supports scalable task generation via synthetic patient instances and structured validators that automatically evaluate outcomes. To measure agent performance, I introduce a τ² (Tau-Squared) benchmark tailored to clinical workflows, designed to test long-horizon reasoning, temporal validity of medical data, and efficient information retrieval. Early results show that even frontier models struggle with the hardest τ² scenarios.