A Drift Detector That Cries Wolf Is Worse Than No Detector

In a recent post, developer Debashish Ghosal discusses the challenges of building reliable drift detection for AI agents within the HivePlane framework. The author highlights that a poorly designed detector—one that triggers false positives—can be more detrimental than having no monitoring at all. Ghosal details three specific failure modes encountered during development, including issues where corpus growth was misinterpreted as behavioral decay, flawed state management during auto-quarantine, and probes that failed to verify actual task performance. The key takeaway is that drift must be measured against consistent baselines rather than raw counts. Furthermore, the author emphasizes that effective monitoring requires robust source-of-truth management, such as using dedicated quarantine records rather than status enums. Ultimately, the article serves as a cautionary tale for MLOps practitioners: if automated systems frequently cry wolf, they quickly lose their utility as reliable security controls.