Agent retrieval doesn't fail only because of search quality. It fails because of how context gets used. Title: Context Engineering Starts with Retrieval URL: 🔎 Overview Using Deep Agents as the example, LangChain sorts retrieval problems into three failures: inefficient, too much, and stale or irrelevant. The case study is a competitive analysis agent that researches hundreds of companies every week. ⚠️ Problems addressed ・Every search burns tokens and fills the context ・Large results crowd out room for the actual work ・Outdated or off-topic material becomes the foundation for later steps 🛠️ Approach ・Narrow with glob and grep first, use vector search only when meaning matters ・Page with read_file (100 lines by default); results over ~20,000 tokens are offloaded to files automatically ・Return only paths, and let a one-file-per-call subagent return just the key facts and sources ・Use date filters for freshness, treat retrieved text as untrusted, and verify citations 📊 Use case To pull one revenue figure from ~40,000 rows, grep for the number and read a few surrounding lines. A news search returns four file paths to the main agent instead of four full articles. #ContextEngineering# #AIAgents#