Bitemporal AI memory solves a problem ordinary RAG cannot see

Most agent memory systems optimize for a simple question: which stored chunks are relevant now?

Production systems eventually need a harder question:

What information was available to the agent when it made a specific decision?

Those questions are not equivalent. If a fact, policy, permission, or architecture decision is corrected later, a latest-state memory system can silently rewrite the context behind an earlier action.