Persona-driven role-playing agents tend to drift from their assigned profile or contradict their earlier statements as the dialogue lengthens, a faithfulness failure we call persona inconsistency hallucination. Existing inference-time mitigations apply reward-guided decoding uniformly at every token, while we observe that inconsistency risk is concentrated on a small subset of turns and is locally predictable, which makes a selective intervention feasible. We propose TaDI (Targeted Decoding Intervention), a training-free method with three components: an Inconsistency Risk Predictor that gates intervention before generation, a Targeted Reward-Guided Decoding module that combines a positive profile reward with a negative commitment reward, and a Commitment Propagation Memory that tracks the agent’s prior claims across turns. We additionally introduce a Type-A/B/C violation taxonomy distinguishing profile violation, history contradiction, and commitment fabrication, used as a diagnostic for attributing where any method gains or loses. Extensive experiments show TaDI is competitive with the strongest always-on baseline while being substantially more efficient and more robust under adversarial interrogation.