Agentic search powered by Elastic's vector database

Lily Adler

Team Lead, GenAI Search Specialist

Elastic

Peter Steenbergen

GenAI Search Specialist

Elastic

As AI applications evolve from assistants to sophisticated agentic systems, one thing has become clear: Context is everything. That's why Elastic, the context engineering platform and leading vector database, sits at the heart of modern agentic search.

In this webinar, we'll explore how Elastic's vector search works, how it integrates with keyword and hybrid search, and why it's the foundation for building AI agents and applications that work with unstructured data. You'll see how our embedding models, rerankers, and search innovations enable semantic matching, similarity search, and hybrid queries at scale.

This session is designed for developers, data engineers, and architects building the next generation of AI agents and applications. We'll close with an open Q&A, so bring your questions and ideas.

Highlights

• How vector search really works: Chunking, embeddings, semantic text search, hybrid queries, and reranking, and how to integrate dense and sparse vectors
• Real-world use cases for AI agents and applications: Semantic matching, document understanding, recommendations, and conversational memory
• How to be enterprise-ready: Billion+ vector scale, hybrid search, multi-modal data support, and built-in analytics that go beyond point-solution VDBs
• Elastic as a context engineering platform and vector database: Why Elastic is the comprehensive vector database and how it underpins agentic search

Additional resources

• See source code and materials from the session in the GitHub Repo
• Test the latest AI search capabilities with AI Playground
• The most widely deployed open source vector database