How to implement context decay so an AI agent forgets stale or incorrect instructions over time?
A year into a project, one agent's memory holds three instructions that should no longer win. It says to install packages with npm, and since June it also says to use pnpm; both are still there, and either can come back first. It holds a workaround for a library bug that was fixed in the spring, and nothing has needed it for months. It says to retry a failing integration test until it passes, and…
Three operations are needed to ensure an AI agent forgets stale or incorrect instructions over time: closing outdated instructions, decaying the rank of unused instructions, and pushing down instructions that were misleading when acted upon. Closing an instruction happens when a newer one replaces it, marking the old one with a date it stopped applying and its successor.
Decay occurs when an instruction is not used, confirmed, or contradicted for a long time, lowering its ranking with time so it stops competing. Pushing down involves giving an explicit negative mark to instructions that misled when the agent acted on them. These three operations ensure that instructions are not deleted but rather marked as closed, decayed, or pushed down, allowing older instructions to still be accessed if needed.
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