Validation, Calibration, and Stress Testing of Governed AI Trading Agents: Evidence Protocols for Digital-Asset Execution Systems

This paper develops a validation framework for governed AI execution agents in digital-asset markets. It separates development-program evidence from launch-candidate evidence; compares governed and ungated AI policies under common baselines; tests signal calibration after costs; applies walk-forward and overfitting controls; evaluates component ablations; and reviews stress-event, venue-execution, and supervisor-intervention records.The framework is applied to a cleared anonymized validation data room. The scientific interpretation is not that future returns are guaranteed, but that risk projection, regime gating, venue-health control, and supervisor escalation can be tested as stabilizing mechanisms. External investment-performance claims require independent reconciliation to source order, account, custody, fee, and funding records.