AI Agent Navigates Contradictory Pharma Documents to Make Cold-Chain Shipment Calls

A developer working in pharmaceutical cold chain has built an AI disposition agent for a fictional drug company, Ilmenau Therapeutics GmbH, as part of the Sanity Challenge. The agent evaluates temperature-excursion events on biological shipments — such as a 2–8 °C product spending 30 hours at 13 °C — and recommends whether to release, quarantine, or reject the shipment. The core problem it solves is document conflict: real quality binders often contain superseded stability limits, mismatched temperature ceilings, and contradictory carrier terms sitting alongside current effective procedures. Rather than relying on keyword search, the agent uses a content graph that links each product to its current stability profile, each shipping configuration to its qualification report, and each route to its risk assessment, resolving conflicts via a hierarchy rule that favors evidence over derived data and stricter regulation over internal policy. The tool was tested against 17 controlled documents and four held shipments, with results showing that disposition outcomes can flip entirely depending on whether the agent reads the superseded appendix or the governing effective summary.