Why Your Repository Shouldn't Be Your Memory

One of the biggest scaling mistakes I've seen in AI projects is treating the repository as the organization's memory. At first it feels convenient: * put notes in the repo * store investigations in the repo * keep failure reports in the repo * save architecture discussions in the repo Six months later: * search results become noisy * agents discover outdated information * important decisions become buried * nobody knows which document is authoritative We eventually learned to separate: **System** * code * runtime state * configuration * operational assets from **Knowledge** * lessons learned * failure analysis * architecture pivots * doctrine * operational observations Repositories are optimized for software. Organizations are optimized for learning. Those are not the same thing. How do you handle operational knowledge that needs to survive multiple refactors and system generations?