I wonder how much my system needs something like this. Between the invisible system memory of my random chats with Gippity, my Matt Pocock skills saving terminology and plans, and whatever else Cursor and Codex do, I don't think I feel a need for more agent memory. I do like how it's exposed and searchable, and not invisible. But I honestly just send my questions/tasks away to my magic agent and eventually it gets it right anyway; do I need more discrete memory my team has to maintain? (That's an earnest question, not disregard for this)

Incredible to watch things come full circle. Next thing you know, someone is going to figure out a binary encoding.

Both can be true: - It's useful to anthropomorphize agents when predicting behavior and - we have to use specific language to specify what we mean.

What does the author mean by "confuse the models" ? Are they talking about not picking right information? Picking the wrong information? Losing their previous context / task?

Part of setting up a proper eval is also deciding what we actually mean ourself. What are we actually optimizing for? It's not, e.g. % confusion, %rubbish, etc.

The article does point to it: retrieval latency, accuracy, etc.