How AI Agent Memory Layers Decide What to Store, Update, or Delete

Most AI agent memory implementations simply embed and store every message in a vector database, causing contradictions when outdated facts are never removed or updated. A more robust memory layer uses four stages: extracting durable facts, retrieving related existing memories, deciding whether to add, update, delete, or ignore each new fact, and writing back the resolved state. The extraction step strips conversational noise and isolates atomic subject-predicate-object facts, making each one independently manageable. At write time, similarity search identifies potentially conflicting existing memories rather than answering user queries, flagging cases like a location change from Austin to Denver. A second LLM call or classifier then resolves the conflict by choosing the appropriate operation, ensuring the memory store reflects current, non-contradictory information.