OpenSearchCon India 2026

AI agents powered by LLMs such as Claude are inherently stateless and rely on external systems to maintain persistent memory. While these models can reason, plan, and call tools, they cannot retain knowledge across sessions on their own, forcing users to repeat information and limiting context-aware interactions.

In this session, we explore how to build context-aware AI agents with persistent memory using OpenSearch. Attendees will learn how OpenSearch’s agentic memory enables agents to store conversations, extract knowledge, learn user preferences, and retrieve relevant context over time.

Through practical examples, we demonstrate semantic knowledge extraction, preference learning, and session summarisation, showing how models like Claude can leverage stored context for personalised, intelligent responses. By the end, participants will understand how to design scalable memory architectures that transform stateless LLM agents into context-aware systems that continuously learn from interactions.