Episode Details

When AI coding agents work in parallel, they can silently undo each other's work — a problem rooted not in bad tooling, but in fundamental amnesia. This episode unpacks three approaches to solving it: vector-backed memory servers, built-in tool memory like CLAUDE.md files, and agent-maintained documentation in the repo. We cover the failure patterns of each approach — stale embeddings, junk-drawer configs, and logs agents write but never read — plus practical fixes like read-before-write enforcement and separating decisions logs from changelogs. If you're running multiple AI coding agents on the same codebase, this episode will save you days of archaeological debugging.