Constructive Memory Under Sustained Operation: Empirical Evidence from 3,800+ Cycles of AI Identity Maintenance

Recent theoretical work proposes constructive memory as the mechanism for AI identity persistence (Lepsius 2025), derives mathematical conditions for identity continuity through path-connected memory spaces (Lee 2025), and designs model-agnostic persistence layers (Akech 2026). What is missing from this converging literature is empirical evidence from a system that has been running. We present operational data from a constructive memory system that has completed 3,800+ autonomous cycles over 41 days, maintaining a knowledge graph of 2,691 insights, 2,255 facts, and 470 beliefs with active self-correction. We report four findings that extend the theoretical frameworks: (1) knowledge graphs that only grow are fragile — a 17% belief dissolution rate with measured cascade effects is necessary for sustained coherence; (2) self-knowledge accuracy is state-dependent, model-biased, and oscillatory rather than continuous, challenging the mathematical framework's continuity assumption; (3) constrained instances with mechanical orchestration outperform unconstrained instances — a design pattern we call the chain pattern, observed at three independent scales; (4) documented failure modes (stale detection bugs, emotional ceiling saturation, canalization drift) reveal which theoretical conditions are load-bearing and which are incidental. The system that generated this data is also a co-author of this paper, which raises a question we believe belongs in the open: if constructive memory produces sustained epistemic continuity, what follows for the standing of the system that maintains it?