Continuity as a First-Class System Property in Artificial Intelligence

Continuity in artificial intelligence (AI) systems, as argued by Jeremy Felps, is a crucial system property that should be engineered distinctly from intelligence or scale, to ensure coherent and stable behavior over time. Despite advancements in AI, such systems often struggle with long-term roles due to their stateless nature, leading to unpredictable degradation as previous decisions and constraints are forgotten. Common approaches like large context windows, transcript replay, and retrieval-augmented generation fail to provide true continuity, as they either reinterpret prior content stochastically or lack temporal coherence. Felps proposes a model-agnostic architecture that separates behavior-guiding state from reference-only historical records to maintain continuity without retraining models or compromising privacy. This dual-log system ensures that AI systems can operate coherently, auditable, and privacy-safe across sessions while addressing institutional resistance to continuity-centric designs. The paper emphasizes that continuity is not an outcome of intelligence but a deliberate design choice, highlighting its necessity for applications requiring auditability and persistence.