User-State Verification in Conversational Commerce: Detecting Journey Hallucinations via Trace Invariants
Conversational commerce agents that personalize assistance based on a user’s transactional state (cart contents, checkout progress, order completion) must model that state correctly, or downstream adaptive behavior will be misaligned with the user’s actual journey. We call mismatches between an agent’s claims and the observable event history journey hallucinations, and study a lightweight verification framework that reconstructs a minimal transactional user model from execution logs and checks agent claims against deterministic invariants. On 90 real sessions across four foundation models, trace-aware prompting reaches 99.5–100% user-state accuracy at 84–99% coverage, while unconstrained prompting produces unsupported state assertions at rates up to 8.5%. In a between-subjects user study (N = 42), verified responses were judged more trustworthy (p =.008, r =.43), better at reflecting journey understanding (p =.039, r =.32), and more often factually correct (p <.001, r =.56). The framework provides a practical reliability layer for transactional user-state modeling, helping personalization and dialog policies operate on verified, not hallucinated, user states.