LangChain explains retrieval techniques for AI agent context engineering

LangChain published a blog post on how retrieval design shapes context engineering for AI agents, using a competitive analysis agent built on its Deep Agents harness as the example. The post covers three failure modes, inefficient searching, loading too much context at once, and irrelevant or stale results, and pairs each with techniques such as glob and grep filesystem tools, paging through large files with read_file offsets, offloading oversized tool results to files, and delegating reading to subagents under a retrieve, offload, delegate pattern. It also warns that retrieved text is data rather than instructions and can carry prompt injection, so outputs should be checked against cited sources.