Agents need memory to hold conversation history, intermediate results, or what they have already tried. Memory can be short-term (current session), long-term (user preferences), or episodic (past events).

Agents can learn from feedback: success/failure signals, user corrections, or rewards. With guardrails, they refine their behavior over time. This is often done via reinforcement learning or fine-tuning on feedback data.