For today’s AI agents, memory is a moat. Every conversation counts, but traditional large language models (LLMs) are stateless — they start each interaction without any context or memory, leaving power untapped and insight lost.
Imagining new paradigms of agent memory has become one of the most urgent frontiers in AI development, allowing for active formation and updating of memories, so agents can use all past interactions in a meaningful way. There are many parallels to human memory, whereby remembering previous exchanges and…
