Knowledge Continuity Engineering: A Framework for Persistent Knowledge Across Human and AI Generations

This paper introduces Knowledge Continuity Engineering (KCE), a proposed discipline that treats knowledge continuity as a first-class engineering concern rather than an archival afterthought. We identify five core problems that existing disciplines fail to address in combination: model discontinuity, human discontinuity, provenance decay, semantic drift, and authority fragmentation. We propose seven design principles (K1–K7), a five-layer reference architecture (Creation → Validation → Persistence → Governance → Continuity), and describe BRIDGRAI — a working system developed independently over 3.5 years — as a reference implementation with 128 IP assets timestamped on Tezos blockchain and 175 automated tests. The extended framework (Sections 8–13) provides implementation guidance with a three-tier adoption model, case studies (one real, two hypothetical), four proposed metrics (PCR, SVR, ACI, DRS), qualitative risk analysis, and an organizational adoption roadmap. The term "knowledge continuity" exists in industry usage (documented in §2.6); the formalization as a discipline with formal problem statements, testable principles, layered architecture, evidence classification scale, and proposed metrics is original to this work. AI contribution: Sections 1–7 drafted with Claude (Anthropic); Sections 8–13 initially structured by Qwen (Alibaba Cloud), reviewed and corrected by the author. Full AI contribution statement included in the paper (Appendix C provides L1 metadata).