I went straight to the short-term vs long-term split and talked about vector stores for retrieval.
Start by clarifying the agent's requirements and constraints, then propose a layered memory architecture (short-term, long-term, episodic) with appropriate storage and retrieval mechanisms. Discuss trade-offs between different approaches (e.g., vector databases vs. relational databases, summarization vs. raw storage) and how they impact performance, cost, and scalability.
Pro tip: Emphasize the importance of memory retrieval relevance and latency, and suggest evaluating memory designs with metrics like recall@k and end-to-end task success. Show awareness of privacy and forgetting mechanisms, which are critical for production systems.
Ask about the agent's use case, expected memory size, latency requirements, and privacy considerations to tailor the design.
Outline short-term (working memory), long-term (knowledge base), and episodic (experience) memory components, each with distinct storage and retrieval strategies.
Select appropriate technologies (e.g., vector databases for semantic search, key-value stores for fast access) and algorithms (e.g., embedding-based retrieval, summarization) for each layer.
Discuss trade-offs between accuracy, latency, cost, and complexity; explain how the design scales with data volume and user load.
Describe metrics (e.g., retrieval precision, task success rate) and processes for updating, pruning, and forgetting memories to maintain relevance and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.