Five layers of agent memory, with JEV named for cheaper decisions
The post splits agent memory into working, episodic, semantic, procedural and forgetting layers, cites Mem0 and Snowflake numbers, and suggests JEV can cut the cost of decisions.
your AI agent might be wasting 90% of its context reading things it already knows. here's how to fix it. AI agent memory has 5 layers: → Working memory: What the agent needs right now. → Episodic memory: Logs of past events, decisions, and failures. → Semantic memory: Facts and relationships stored for future use. → Procedural memory: Skills and approaches that worked before. → Forgetting: Removing stale facts and resolving contradictions. The last one is the most overlooked. Without forgetting, your agent's memory becomes a pile of outdated information it can't trust. The reported numbers are worth a look: → Mem0: 1,800 tokens per query instead of 26,000, with 91% lower latency. → Snowflake: An ontology layer reportedly improved accuracy by 20% and cut tool calls by 39%. And there's another side to the problem. Memory reduces what your agent needs to read. Jev can reduce what it costs to make decisions. The goal isn't to give agents more context. It's to give them the right context, at the right time, without paying to rediscover the same things every day. https://x.com/AnnatarXBT/status/2114319310005150101/video/1
This post quotes another post: x.com/arindam_1729/status/2112892916159111359