Implementing Agentic RAG using Elasticsearch & Langchain

Chidambaram Chidambaram

The session Implementing Agentic RAG using Elasticsearch and LangChain is now available to watch on-demand.

Led by Chidambaram Chidambaram, Senior Customer Architect at Elastic, the session focuses on building advanced retrieval augmented generation (RAG) systems that go beyond static prompts, and is designed for teams and practitioners evaluating agentic approaches to RAG or refining existing AI architectures.

What’s in store

• Implementing Agentic RAG with Elasticsearch at the core

• Using LangChain to build workflows that interact, retrieve, and reason

• Powering faster, context-aware answers across domains

• Building AI agents designed to retrieve and reason, not just respond

Stream the on-demand session to examine how AI agents retrieve information, reason over context, and support dynamic decision-making using Elasticsearch as the data backbone and LangChain for workflow orchestration