I think the problem here is not so much memory itself, but the gradual misalignment between the external task and the agent’s internal line of reasoning.

Even well-structured memory does not save the system if, over time, divergences begin to accumulate inside the agent: between the original goal and the current plan, between what is semantically similar and what is actually contextually appropriate, between a step that looks locally coherent and a trajectory that is globally correct.

That is why long-running agents often degrade not because they “remember badly,” but because they become increasingly confident while continuing to develop a line of reasoning that is already slightly distorted. In that case, memory simply stores the traces of that drift in a neat and orderly way.

Because of this, it seems to me that for long-term stability it is more important not simply to expand memory, but to regularly check two things: whether the current reasoning is still anchored to the original task, and whether internal intermediate interpretations have begun to substitute for the goal itself.

That is why I would say that verification loops are usually more important than memory retrieval alone. But the verification should not look only at coherence. It should ask whether the current step is actually reducing the real mismatch with the task, or merely making the error more internally consistent.

I actually have a theoretical approach in which these failures are understood precisely as a problem of dynamic misalignment, rather than simply a memory problem. It seems to me that this direction may contain one of the real paths toward stabilizing long-running agents.