The way we store memories is to adjust the weights in our brain (as far as we know). The way models store memories seems to be to write them down on a scratchpad someplace (details vary, but roughly this is what they're doing).

The article explicitly excludes this from consideration early on though:

By “agent memory,” we mean knowledge that Claude gains from experience....It excludes other ways models can learn or manage context, such as changes to their weights

Those seem fundamentally different processes, and adjusting weights does seem a much better idea after all, but then in that world everyone has a different AI and how do you apply updates? For a company like Anthropic this is of course a big problem and that is probably why they don't like that route. Could we store weight deltas or something similar and reapply them over a different model?