Our agent saved 80,000 characters of lessons. The next agent never read them.

We added a pretty reasonable feature to our coding workflow: when a reviewer asked for changes, save the feedback so future agents could learn from it. In about 12 hours, one project collected 20 entries, roughly 80,000 characters. Then we checked how the next coding agent actually started its task. It read our coding standards and pinned notes. It never read the field where all that reviewer feedback was going. We had built the saving part and missed the reading part. The project record kept growing, with no path for those lessons to influence the next implementation. We disabled automatic appending by default. Extracting useful rules and connecting them to the next task became separate work. That changed the questions I ask about agent memory. Where is this information read? At what point in the task? And how would I check whether it prevented the same mistake? Saving a conversation is easy. Deciding what should affect the next decision is where I think the real work starts. For people running agents across multiple sessions: how do you check that their saved lessons actually change what they do?