Why Agents Stop Learning Too Soon
We repeatedly observed the same failure.
When an agent needs to learn about its environment, it sometimes takes actions that look suboptimal in the short term: overstocking to observe demand, altering cadence to measure supplier response, testing substitutions to understand behavior. These actions are not mistakes. They are deliberate attempts to learn.
In monolithic agent designs, these actions rarely survive. The agent begins the probe. Short-term reward drops. The objective function flags a deviation from reward. The agent corrects it. Mid-execution, the learning attempt disappears.
This is not an implementation bug. It is a structural property of these architectures, where execution and learning commitments are optimized within the same objective. Actions whose purpose is information are indistinguishable from mistakes to an optimizer focused on reward.
Prompting does not resolve this. You can tell the agent it is experimenting. It may even acknowledge this. But instructions compete with objectives — and objectives win.
This failure repeated often enough that we stopped treating it as a tuning problem. If learning requires time-extended commitment, that commitment cannot live entirely inside the same mechanism that is optimizing execution.