AI agent memory is not a single database or an endlessly growing prompt. According to Unite.AI, it is a designed system for retaining working context, episodes, facts, and learned procedures—and for retrieving the right information at the right time. That framing shifts the focus from storage size to retrieval relevance.
The article breaks agent memory into four types: short-term, long-term, episodic, and semantic. Short-term memory handles the immediate working context, while long-term memory holds durable knowledge. Episodic memory stores past experiences, and semantic memory stores general facts. Together, they let an agent decide what to keep and what to recall.
Because the source is a single explainer, there are no conflicting views to compare. The main takeaway is that memory in AI agents is an architectural choice, not a simple append-only log.