Agentic Search for Context Engineering — Leonie Monigatti, Elastic

Getting context into an LLM is not just a retrieval problem. It is a search problem. This workshop digs into the part of context engineering that usually gets waved away: how agents actually decide what to pull from files, databases, memory, and the web, and why that choice often matters more than the model itself. Across semantic search, general-purpose database tools, shell-based retrieval, and agent skills, Leonie Monigatti shows where each search interface works, where it breaks, and how to combine them into a more effective retrieval stack. If you're building agents and trying to make retrieval less brittle, this is a practical guide to the real mechanics behind agentic search. Workshop repo: https://github.com/iamleonie/workshop... Speaker info: https://x.com/helloiamleonie   / 804250ab   Timestamps: 0:00:00 - Introduction and Welcome 0:00:51 - Defining Context Engineering and the role of Search 0:02:21 - Historical context: From RAG to Agentic RAG 0:04:30 - Context sources (local files, memory, databases, web) 0:06:30 - Introduction to the Shell tool and its versatility 0:08:50 - Failure modes in agentic search 0:10:41 - The importance of tool descriptions and parameter design 0:13:53 - Code Demo: Simple semantic search and its limitations 0:23:26 - Code Demo: General purpose database query (ESQL) 0:28:36 - Code Demo: Adding Agent Skills for better interaction 0:34:42 - Code Demo: Using the Shell tool for file system retrieval 0:41:26 - Code Demo: Integrating custom CLIs (Gina Grap) 0:44:42 - Practical recommendations for building a search tool stack 0:49:16 - Q&A Session begins