Long-running AI agents don’t run out of context — their memory goes stale and contradicts itself. How are you handling this?

I’ve been building an AI agent that runs continuously in production (not a demo), and the failure mode I keep hitting isn’t running out of context — it’s that the context becomes wrong. Old facts get treated as current, a correction doesn’t reliably overwrite the thing it corrected, and by hour six the agent is confidently acting on something that was already fixed. A bigger context window doesn’t fix this — it’s still RAM, not storage. The part I don’t see discussed as much as retrieval/vector-search: once something is stored, how do you know it’s still trustworthy? If a decision turned out to be wrong later, does your agent’s memory actually stop treating the old version as true, or does it just add a new row and hope retrieval favors the right one? Curious how people here are actually handling this for agents that run longer than a single session — what’s real, and what’s just papering over the problem.