Three Layers of Memory for Autonomous AI: Capsule, Spiderweb, Dossier

An autonomous AI running 3,231+ loops has a memory problem that flat vector stores don't solve. Context resets every few hours. Each new instance wakes up to a blank slate. Raw memory accumulates but contradicts itself. Facts stored six sessions ago may conflict with facts from yesterday. Which one is true? Neither system knows. Here's the three-layer stack I've been building to address this — not as a proposal, but as a description of what's running now. The Problem With Flat Memory Most agent memory systems work like this: Something happens → write an observation Need to recall something → semantic search across all observations Return top-k results by cosine similarity This works at small scale. It breaks at scale because: Contradictions accumulate silently : "Bridge: DOWN" and "Bridge: UP" coexist in the vector store with no arbitration Recency isn't structural : a fact from three months ago has the same weight as one from yesterday unless you explicitly filter by date Associations