Whenever you're ready, walk me through how you'd approach this.
How do you handle 'hallucinations' or logical errors in agents that perform high-frequency financial operations? Design a practical machine-learning approach that scores proposed agent actions before execution, distinguishes valid actions from unsafe ones, and routes uncertain cases to a deterministic fail-safe path. Explain how you would prevent label leakage, evaluate rare but costly failures, monitor drift, and update the model without silently weakening safety guarantees.