CasePath: An Agentic, Process-First Architecture for Determining Evidence Requirements

Large language model (LLM) agents in rule-governed workflows must choose actions that apply to the current case. In evidence collection, a single fact can make a receipt required, irrelevant or premature. Direct checklist generation leaves the model to judge that fact and choose the action in the same output, even when the rules are in context. We introduce CasePath, a process-first architecture that separates these decisions. The model assesses case conditions and documents; executable process control activates the applicable requirements, derives their evidence needs and holds requests whose conditions remain unresolved. Each request retains its justification. Two synthetic claim studies test this design. On 27 held-out theft-case pairs differing in one fact, CasePath makes 15 wrong checklist changes, versus 27 for direct generation and 41 for generation with the process graph in context. On 108 matched tenancy cases with model judgments fixed, checking inherited branch conditions removes 98% of unnecessary requests and more than halves immediate requests, retaining all 635 valid ones. We release both datasets, structured references, predictions and scorers. Executable process state gives agents a control layer between model judgment and evidence requests.