The rapid evolution of AI capabilities has developers both excited and challenged. Large language models (LLMs) and vector search have propelled us into the semantic revolution, moving beyond keyword matching to concept-driven retrieval. LLMs have also transformed how we interact with data, using chat interfaces to turn natural language into distilled, context-rich summaries.

Now, agentic AI workflows are emerging — systems that can understand a request, reason through the steps, and select the right tools to iteratively achieve a goal. This shift demands a new focus: context engineering. Instead of relying solely on prompt engineering, we must ensure AI agents have access to the most relevant, precise, and efficient information. Hybrid search, combining lexical precision with semantic understanding, is the most powerful way to surface that context. The Elasticsearch Platform provides the flexibility and performance to make this possible.

This white paper is designed for developers, data engineers, and AI practitioners who want to build smarter, more reliable agentic AI systems.

Highlights

• Hybrid search synergy: Merge lexical precision with vector-based semantic search.
• Advanced reranking: Boost accuracy with multistage relevance scoring.
• Context optimization: Aggregate, filter, and refine data for AI context windows.
• Built-in security: Protect sensitive data with role- and attribute-based access control.
• Elastic Agent Builder: Create custom AI tools with expressive ES|QL queries.