Knowledge Age Routing: Matching Queries to the Right Temporal Layer in Production AI
Here is a scenario that surfaces in production more often than anyone likes to admit. A user asks your AI assistant what the current interest rate policy is. Your RAG system fetches a highly relevant Federal Reserve document—semantically it scores 0.91 similarity—and the model confidently returns an answer. The answer is six months out of date. The RAG index was last refreshed in October. The parametric knowledge is older still. A live API call would have returned the correct current figure in 400 milliseconds, but nobody wired up the routing logic to ask: how old is this question's answer allowed to be?
That failure is not a retrieval failure. It is a temporal routing failure. The system had access to correct information somewhere in its stack. It just sent the query to the wrong layer.
