There’s a significant gap in current AI memory technology, with most tools only capable of storing facts and retrieving them through similarity searches. However, this approach falls short when it comes to agents that need to learn from experience and adapt to new situations.

The key to unlocking more advanced AI memory lies in the ability to recall not just facts, but also experiences and outcomes. For instance, an AI assistant that can learn from its mistakes and apply that knowledge to new situations is far more valuable than one that simply remembers a user’s preferences.

In practice, current AI systems often struggle to learn from mistakes, forcing them to start from scratch and repeat the same errors. This lack of experiential memory is a significant limitation, and one that is rooted in the way AI memory is currently designed.

Cognitive science recognizes three distinct types of human memory: semantic (facts and knowledge), episodic (personal experiences with context and outcomes), and procedural (knowing how to do things, refined through practice). To make AI memory truly useful, these same types of memory need to be integrated into AI systems.

To achieve this, transparency and control are essential. Users should be able to see and edit what their AI remembers, and have control over where their data is stored. Additionally, AI memory should provide enough value to justify the potential risks, such as remembering key details about a client’s issue and the fix that resolved it.

Photo by John Tekeridis on Pexels
Photos provided by Pexels