Memory for agents ain't here yet

Each day I slide on Reddit, Twitter, etc and everyone and their grandma is pushing a RAG wrapper as some God sent memory tool from on high. All of them follow the same prescription: Yeah, we have vector search but our reranker or graph helps with accuracy. We got MCP because the model should be asking us for the information. Better markdown file management. Everything is local. Or some new bigger embedder, reranker, rrf, etc. But why hasn't anyone seen mass adoption yet? I think builders are patching together different pieces of the puzzle and hopes the memory layer gives you back what you asked for. No one is stepping back and trying to understand the problem. I believe for a true system to work you can't depend on the model asking (MCP) because models are trained on RLHF which creates a bias wherein models optimize for approval over accuracy. Nor can having multiple skills files as that's just more context bloat for the model to reason against which now takes attention away from the actual thing you were asking about. I don't think they can depend on extraction after the fact either because think about when you were asked to remember something, depending on how you were asked, you could remember things that never happened. So what does an ideal system actually look like?