Most of you have likely experienced the context window filling up and token costs skyrocketing as AI agent tasks get longer. Typically, we use a sliding window to cut off past conversations or apply simple summarization, but this always creates a trade-off where important context is lost.

A 'Reversible Forgetting' pattern proposed in a recent paper offers a fairly practical alternative. The agent decides for itself to swap long execution logs for a summary, while keeping only a reference pointer so that if the detailed context is needed later, it can restore the original log. Instead of just deleting it, it's more like folding it away for a while.

When building complex multi-step agents, this pattern can significantly reduce token consumption while preventing context pollution, making it worth considering when implementing AI SDKs or agent workflows.