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Design a two-tier memory architecture for a long-running assistant agent
Design a memory system for an agent that runs {{deployment_context, e.g. a persistent assistant across many sessions with the same user}}. I need a concrete design, not general advice. Cover:
1. What goes in short-term (working/session) memory versus long-term (persisted) memory, with specific examples relevant to this agent's task, and the rule for when something graduates from one to the other.
2. Write format for long-term memories: structured fields (not raw transcript dumps) so they're retrievable and don't bloat the context window, give me the actual schema.
3. Retrieval strategy: how the agent decides what to pull into context for a given turn (recency, relevance search, explicit user reference), and how to avoid retrieving stale or contradicted memories.
4. Conflict resolution: what happens when a new fact contradicts a stored memory (e.g. user says their preference changed) - should it overwrite, version, or flag for confirmation.
5. A concrete forgetting/pruning policy so memory doesn't grow unbounded, with a specific trigger (age, low retrieval frequency, explicit user request) rather than 'periodically clean up'.
Agent context: deployment_context
Expected memory volume/session frequency: volume_context