A conceptual framework for Zero-Error AI agents using Digital AI Passport approach

Artificial Intelligence (AI) systems are increasingly deployed in high-stakes domains where even minor errors can have severe consequences. Existing lifecycle documentation frameworks—such as Model Cards, Datasheets for Datasets, and AI FactSheets—provide transparency but lack continuous performance monitoring, lifecycle-wide traceability, and mechanisms to preserve full operational context. This paper proposes the Digital AI Passport (DAIP)—a conceptual framework adapted from the Digital Product Passport (DPP) model in manufacturing—to establish a structured, auditable identity for AI models across their lifecycle. At its core is the Zero-Error AI Agent, a concept transferred from the Zero Defect Manufacturing (ZDM) domain, which operates through a two-level triggering mechanism: monitoring and predicting changes in working conditions, followed by detecting or predicting performance loss to enable corrective or preventive actions. This approach combines proactive and reactive strategies to achieve near zero-error operation. Although conceptual, the DAIP provides a foundation for future implementations to validate its feasibility, scalability, and advantages over existing AI governance tools.