Store an AI agent's medium-term rolling summaries in relational databases. Vector stores excel at retrieving atomic facts, but summaries are narratives whose value lies in continuity. Conflating these memory tiers adds unnecessary complexity and results in sub-optimal performance.
Avoid building complex intra-store authorization systems. Instead, treat each memory store deployment as a single tenant or group. This shifts access control to the infrastructure layer, creating a simpler, more robust security model that is followed by major cloud providers.
Every user conversation is a potential write path for memory poisoning attacks. To mitigate this, AI systems must derive memory access scopes from authenticated server-side identities and never from arguments supplied by the model, its tools, or the user.
In multi-tier AI memory, designate raw conversation logs as the durable source of truth. All other forms—summaries, facts, embeddings—should be treated as recomputable projections. This design allows for recovery from data loss and adaptation to new extraction strategies.