Join us for an intensive technical session exploring the architecture and implementation of persistent agent memory systems using OpenSearch. This specialized workshop focuses on building sophisticated memory frameworks aligned with Amazon Bedrock's Agent Core Memory patterns, enabling AI agents to maintain context across interactions and learn from past experiences.

 Through expert demonstrations and technical discussions, participants will learn how to design and implement memory persistence layers that enhance agent capabilities while maintaining performance at scale. Discover how to effectively leverage OpenSearch's capabilities to create memory systems that give your AI applications a significant competitive advantage through improved contextual understanding and response relevance.

As organizations increasingly deploy RAG systems and large language models in production environments, the need for visibility into their operation becomes critical. This session will also explore the unique challenges of monitoring generative AI systems and provides practical approaches for implementing metrics, logging, and evaluation frameworks that deliver actionable insights. The participants will learn how to track retrieval quality, response accuracy, and operational performance—transforming opaque AI behaviors into transparent, measurable processes that can be continuously improved.