Multi-agent AI deliberation systems produce verdicts that are necessarily snapshots: they reflect what the evidence establishes at the moment of deliberation. New data releases, regulatory decisions, experimental replications, and market events may all change the evidentiary basis of a verdict after the session closes. Yet existing AI systems that produce research conclusions have no mechanism for characterizing the conditions under which those conclusions should be treated as superseded. We describe the Reopen Conditions mechanism—a structured verdict fragility output that is a required component of the final delivery in the Augle seven-agent deliberation ensemble. The Synthesizer agent’s Round 3 output contract mandates generation of between two and five reopen conditions, each specifying a precisely defined observable trigger event, a likelihood assessment, the specific claim whose grade would change, and the direction of change. General uncertainty acknowledgments are expressly prohibited as reopen conditions: each trigger must be a specific, observable, external event. A product-modeaware framing requirement applies distinct trigger conventions in Markets mode (data releases, regulatory decisions, price movements) versus Academia mode (new publications, replications, methodological critiques). Reopen conditions are stored as structured objects in the session corpus record, enabling an outcome tracking pipeline to monitor for trigger occurrence and generate follow-on session proposals when triggers resolve—creating a compounding calibration loop in which verdict fragility drives corpus expansion. Companion papers: Kelly, C. Zenodo DOI 10.5281/zenodo.20619123. Kelly, C. & Saxena, S. (2026). “Ground-Truth-Mapped Reasoning Corpus Generation via Prediction Market Contract Pairing.” Zenodo, June 2026.
Kelly et al. (Mon,) studied this question.