Recalibrate: Metacognitive Monitoring for Persistent AI Agents

Persistent AI agents that maintain long-term relationships with users face a fundamental challenge: cognitive drift—the gradual divergence between the agent's internal model of the user and the user's actual state. Current memory architectures (Retain, Recall, Reflect) handle information storage, retrieval, and pattern extraction, but lack mechanisms for detecting when the agent's understanding has become stale or incorrect. We propose Recalibrate, a fourth memory operation that implements metacognitive monitoring—the ability of an agent to detect inconsistencies between its stored beliefs and observed reality, then autonomously correct them. We present a framework combining autonomous drift detection with human-in-the-loop calibration, along with calibration strategies and evaluation metrics. We validate the approach through both a real-world case study of a persistent AI agent that forgot its own identity and a synthetic benchmark where Recalibrate achieves 100% drift detection across four drift types with 0% false positives, compared to 0% detection in agents without explicit drift monitoring.