[D] Is lossy compression acceptable for conversational agent memory? Every system today uses knowledge graph triples — here's why I think that's wrong.

Been thinking about this and want to know if others have hit the same issue. The dominant approach for agent memory (Mem0, Zep, most RAG pipelines) extracts entity-relation triples from conversations: [Borrower] --prefers--> [WhatsApp] [Borrower] --outstanding-balance--> [₹45,000] It's clean and queryable. But it's lossy by design. Three things you lose: Anything non-triplable "Agent's attempt to reschedule met resistance, call ended inconclusively" this doesn't fit SOP. You either mangle it or drop it. Sequence and causality "Prefers WhatsApp" means different things said casually vs.