Agentic AI Interview Questions
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Q6EasyConcept

Explain short-term vs long-term memory in agents.

30-second answerSay your answer out loud first, then reveal.

Short-term (working) memory

  • The message history plus tool observations in the current run.
  • Limited by the context window. Even with large windows, quality degrades as context fills ("context rot"), and cost grows with every token re-sent.
  • Managed by trimming, summarising older turns, or offloading large tool outputs to files and keeping only a reference.

Long-term memory

Long-term memory is often divided along cognitive-science lines:

  • Semantic: facts, e.g. "User prefers Python. Company uses Postgres."
  • Episodic: past experiences, e.g. "Last time this deploy failed because of X."
  • Procedural: how to do things. Learned instructions and updated system prompts.

How long-term memory works in practice

  1. Write: after a turn, or in the background, an LLM extracts memory-worthy facts.
  2. Store: in a vector DB, key-value store, or plain files/documents per user.
  3. Read: at the start of a turn, retrieve relevant memories by semantic search, recency or explicit keys, and inject them into context.
  4. Maintain: update conflicting facts and delete stale ones.
Example. A tutoring agent remembers that "the student struggles with backpropagation" across sessions. Without long-term memory it would start over every time.

Common mistakes

  • Saying "we just store the whole chat history in a vector DB." That gives noisy retrieval and no conflict resolution.
  • Ignoring privacy, i.e. what should never be stored.

Follow-ups to expect

  • How do you handle a memory that becomes outdated? (See Q29.)
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