Building and maintaining AI agents that can perform optimally over time is one of the main challenges in the field of artificial intelligence. Memory and state management in these agents, especially during long-term use, plays a key role and ensures their performance.
In this article, five widely-used and effective architectural patterns for maintaining the memory and state of AI agents are introduced. These patterns enable optimization of stored data, improve access to information, and ensure coordination among different states of the agents, thereby increasing confidence in their performance over extended periods.