About

AI agents are powerful—but they forget, they lose context, and they’re expensive to run at scale.

Hear from LangChain, Tavily, Augment, and Cognee on how the industry is tackling agent memory and context for AI agents head on.

Key topics

1. Why memory is critical for building reliable, cost-efficient AI agents
2. The tradeoffs between approaches like retrieval, semantic caching, and long-context models
3. How context engineering is emerging as its own discipline
4. Where AI memory is headed next — and how Redis fits as the foundation for scalable solutions