AI agent memory is the capability of an AI system to retain and utilize information from past interactions—both within a single conversation and across multiple sessions—to provide contextually relevant, personalized responses.

Memory transforms AI from a stateless responder to a relationship-aware agent. Within a conversation, memory means the AI recalls what was discussed earlier: "As I mentioned, your order shipped yesterday." Across conversations, memory means recognizing returning customers: "I see you contacted us about this last week—let me check the status."

Implementing memory involves tradeoffs. Longer memory windows improve context but increase inference cost and latency. Cross-session memory improves personalization but raises privacy considerations—customers may not expect the AI to remember previous conversations. Memory scope decisions should be explicit and, ideally, customer-controllable.

For complex support interactions, memory is essential. A multi-step troubleshooting flow breaks down if the AI forgets what the customer already tried. Customer expectations are shaped by human interactions where memory is assumed—AI without memory feels broken, not just limited.

Related terms: Context window, Multi-turn conversation, AI personalization