As AI agents evolve from stateless responders into persistent, goal-directed systems, memory has become a central design challenge. The question is no longer just what agents should remember — but what they should forget.

In this webinar, we’ll explore how memory architectures for agentic AI have evolved under the constraints of finite context windows. We’ll look at the shift from purely in-context approaches to external memory systems such as databases and retrieval-augmented generation, the rise of vector search for semantic recall, and emerging graph-based models that capture relationships between experiences.

At the heart of these approaches is a key tradeoff: agents must accumulate knowledge over time while operating within strict computational and contextual limits. This creates the need for selective forgetting—techniques for compression, consolidation, and eviction that mirror aspects of biological cognition.

We’ll finish up by discussing open challenges in agent memory design, including cross-session continuity, multi-agent consistency, and the privacy implications of persistent AI memory.