AI / Agentic AI Interview Questions
Explain the lifecycle of an agent's memory from short-term context to long-term storage?
flowchart LR
A[Active Session: Short-Term Memory] --> B[Relevance Filtering]
B --> C[Embedding or Structured Note Generation]
C --> D[Stored in Vector DB: Long-Term Memory]
D --> E[Retrieved via Similarity Search in Future Sessions]
- Active session: information starts in short-term memory, living inside the current context window as the conversation or task unfolds
- Filtering: not everything is worth keeping, so a decision, whether rule-based or model-driven, determines what's significant enough to persist
- Encoding: selected information is converted into an embedding, or in some architectures, an LLM-generated structured note with keywords and context
- Storage: that encoded memory is written to a long-term store, commonly a vector database
- 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.
What determines what information moves from short-term to long-term memory?
What can advanced memory architectures do with new incoming information?
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