You upgrade your agent's model, and the next day its answers are quietly worse. You have probably felt this without knowing why. Most teams treat a model swap as a simple replacement. But an agent's accumulated memory was written around the old model's quirks and interpretations. The moment a new model inherits that memory, performance can degrade silently, with no test catching it. So researchers put four memory formats head to head across an actual model swap: raw history, RAG, model-compressed notes, and fixed-schema knowledge graphs. The results diverge sharply. Fixed-schema knowledge graphs came through almost untouched (just ±0.0004 points of change), while compressed notes swung by as much as 13.28 points depending on which model wrote them and which read them, and a partially migrated embedding index silently forfeited 58% of the accuracy gain a full re-embedding would have delivered. Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability Maybe it is time to stop calling it a model swap and start calling it what it really is: a memory migration project. #AIAgents# #MemorySystems#