Overview
Without persistence, every session pays the same discovery cost. The agent repeatedly searches for the same files, retries the same broken integrations, uses the same inefficient editing pattern, or overlooks the same available capability.
A learning loop converts time and token expenditure into reusable intellectual capital. The immediate task produces an output, but it also produces a better operating system for the next task.
As the instruction layer accumulates valid constraints, the search space narrows and similar work can become faster.