The agent failure mode no eval catches: acting on a fact that was true when it was cached and wrong when it was used

Most agent reliability tooling checks one thing: is this answer faithful to the context it was given? That catches contradictions and made-up citations. It structurally cannot catch staleness, because a stale belief is perfectly consistent with itself. It's just out of date. Concretely: an agent reads a cached "contact's title is VP of Engineering" that was true last quarter, the person changed jobs, and the agent personalizes a send on a title that no longer holds. No exception, no failed assertion, nothing for a test to catch. Coherent and wrong. I think it sits in a blind spot between two layers. Data engineering treats it as a freshness/TTL problem at ingestion. LLM evals treat it as a groundedness problem at generation. But a belief can be fresh enough at ingestion and grounded in its context and still be wrong at the instant of action, because the world moved in between. The framing I've settled on is currency vs consistency as separate axes. Consistency: does the answer match its source. Currency: is the source still true right now. Grounding checks the first. Almost nothing checks the second at action time. How do people here handle this? TTL on everything and re-fetch? A verifier pass before high-risk tool calls? Human-in-the-loop on writes only? Disclosure: I work on this problem so I'm biased. Mostly I want to know whether others see it the same way or think it's a non-issue.