Every team trying to give AI agents memory is solving the same three problems badly. After running production agent memory for several months across two codebases, here are the failure modes I keep hitting and the one pattern that actually works. Failure 1: Embed everything as vectors and call it memory The instinct is reasonable. You have a vector database, you have embeddings, you have a retrieval API. Memory looks like "stuff a conversation in, get relevant chunks out." So you dump every session's transcript, every decision, every code review into the same embedding store and retrieve by similarity. This breaks because facts and conversations have different retrieval shapes. Ask the agent "what did we decide about JWT vs opaque session tokens?" and the embedding store returns five things kind-of-about-tokens by vector similarity. Three of them are old debate snippets. One is a tangential comment from a different feature. The actual decision record is in there somewhere, ranked alongside the noise.…