Building Memory for AI Agents: From Episodes to Knowledge
Why episodic, semantic, procedural, and working memory should not be dumped into the same bucket.
An AI agent completes a task successfully. It finds a flight, compares the options, applies the travel policy, and books the right itinerary.
What should it remember?
Everything, of course. Embed the conversation, persist the tool calls, store the final response, the screenshots, preferences, policy documents, and all the steps taken. When a similar request comes up later, retrieve the most relevant pieces and put them back into context.
It looks like memory. In practice, it becomes a costly junk drawer.
The problem is not that it stores too few things — it treats entirely different types of information as if they did the same job. A user's seat preference is not the story of their last booking. The refund policy is not the sequence of steps to refund a flight. The agent's current scratchpad...
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