Explain the lifecycle of an agent's memory from short-term context to long-term storage?

1. Active session: information starts in short-term memory, living inside the current context window as the conversation or task unfolds
2. Filtering: not everything is worth keeping, so a decision, whether rule-based or model-driven, determines what's significant enough to persist
3. Encoding: selected information is converted into an embedding, or in some architectures, an LLM-generated structured note with keywords and context
4. Storage: that encoded memory is written to a long-term store, commonly a vector database
5. Retrieval: in future sessions, a similarity search pulls back relevant stored memories to enrich the agent's current context

Some more advanced memory architectures go a step further, letting new incoming information trigger updates to existing stored memories, creating an evolving, interconnected network rather than a simple append-only log.

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