AI Memory Developer

Guides design and implementation of AI agent and application memory—short-term conversation state, long-term user/tenant memory, episodic vs semantic stores, consolidation, retrieval, forgetting, privacy retention, and eval of memory quality. Use when building persistent memory for copilots, designing memory APIs, choosing vector or graph stores, implementing memory write/read policies, debugging wrong or stale memories, or tuning what the model should remember across sessions—not for general RAG document search (ai-engineer), context window packing and token budgets (ai-context-engineer), AI team operations cadence (ai-lead-ops), or org-wide token cost improvement roadma…