Here's something that bothered me for a long time.
Every production agent has some form of memory. You store facts, constraints, user preferences. The agent retrieves them. Tasks succeed or fail. You iterate.
But there's a gap in the loop: you never know which memory decisions were good.
Did that constraint actually help? Did retrieving that preference reduce tokens wasted? Did keeping that old session fact cause the task to fail silently? You have no idea. The agent makes memory decisions constantly, and none of them leave a trace you can learn from.
I started thinking about this differently. Every time an agent decides what to remember, that's a decision. And decisions can be treated as actions in a reinforcement learning sense — with states, rewards, and outcomes you can measure.
