Design agentic RAG architecture that reasons, not just retrieves

Agentic retrieval augmented generation (RAG) architecture goes further than retrieval. Combine hybrid search and large language model (LLM) reasoning to build RAG systems that adapt, act, and scale.

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Getting started with agentic RAG using Elasticsearch

The shift from retrieval to reasoning requires a different approach to building AI systems. This on-demand webinar covers the hybrid retrieval strategies, LLM tool use patterns, and agent use cases that make agentic RAG architecture precise and scalable.

See a live demo of an agentic knowledge base built on Elasticsearch and walk away with the patterns to start shipping your own.

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Meet the speakers

Learn from Elasticsearch experts who help technical audiences navigate complex AI infrastructure decisions.

  • Han Xiang Choong

    Senior Customer Architect, Elastic

  • Aditya Tripathi

    Principal Product Marketing Manager, Elastic

How to build production-ready agentic RAG

Production-ready AI requires agents that reason, retrieve, and act. Get a practical framework to build robust agentic RAG using hybrid search, optimized pipelines, and deliberate architecture.

  • Evolve to agentic RAG

    LLMs have moved well beyond basic chatbots. Agentic retrieval augmented generation is the architecture powering production-ready AI that reasons over live enterprise data.

  • Build agents that act

    Intelligent agents use tools and decision-making loops to go beyond static retrieval, enabling agentic RAG systems that dynamically respond to complex enterprise queries.

  • Deploy hybrid retrieval

    Hybrid search is foundational to agentic RAG. Combining keyword and vector search at scale delivers the precision and recall production AI applications demand.

Agentic RAG architecture starts here

Retrieval is just the start. Get the agentic RAG architecture patterns to build systems that reason, act, and adapt.

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