I have been building agentic systems in enterprise for a while and the same problem keeps showing up: agents forget things, confuse old context with new, and accumulate stale memories until retrieval becomes useless. Then I noticed something. These are not engineering bugs. These are the exact same failure modes described in Alzheimer’s research. Not as a metaphor. Structurally. In Alzheimer’s the hippocampus degrades first. It consolidates short-term memories into long-term. When it breaks, the patient can remember their childhood but not breakfast. Now look at an agentic system. The context window is working memory. RAG is a consolidation mechanism that does not actually consolidate. Vector stores are long-term memory without a hippocampus curating what goes in. Same architecture. Same failures. Different substrate. So I went through the cognitive science literature. Five principles with decades of experimental validation that translate directly to agent memory design: Memory is a taxonomy, not a single store (Tulving 1972). Episodic, semantic, procedural. Every agent I have seen collapses these into one flat retrieval pool. The noise alone kills performance. Depth of encoding determines durability (Craik & Lockhart 1972). A raw log decays. A memory encoded with intent, causality, and relationships survives. Most agents store shallow. Retrieval strengthens memory (Roediger & Karpicke 2006). The testing effect. Active recall consolidates better than passive injection. The standard agentic pattern is inject-and-forget. No feedback loop. Forgetting is a feature (Ebbinghaus 1885). A system that never forgets accumulates noise until retrieval degrades to uselessness. Agents today hoard everything. That is not robustness. That is the opposite pathology of Alzheimer’s. Redundancy creates resilience (Stern 2002). Cognitive reserve. More retrieval paths per memory means more resilience. In agents: multi-anchor indexing instead of single-vector lookup. None of these require AI-specific papers. They describe properties of memory systems regardless of substrate. I turned this into a spec called COGNITION.md. It is a declarative contract (same pattern as CLAUDE.md or SKILL.md) that defines how an agent should encode, consolidate, retrieve, prune, and verify its memories. Framework-agnostic. Not an SDK, not a runtime, not another memory product. Just the cognitive contract that any memory implementation can adopt. Repo: https://github.com/arananet/cognition.md It is early (v0.2 draft stage). Would love feedback from people working on agent memory, cognitive science, or anyone who has hit the same wall with long-running agents losing coherence. submitted by /u/arananet
Originally posted by u/arananet on r/ClaudeCode