Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding Large Language Model (LLM) agents in domain-specific knowledge. The standard approach requires selecting an embedding model, designing a chunking strategy, deploying a vector database, maintaining indexes, and performing approximate nearest neighbor (ANN) search at query time. We argue that for domain-specific knowledge grounding --- where the vocabulary is predictable and the corpus is bounded --- this entire stack is unnecessary. We present Knowledge Search, a two-layer retrieval system composed of (1) grep with contextual line windows and (2) cat of pre-structured fallback files. Deployed in production across 20 specialized LLM agents serving three knowledge domains (Traditional Chinese Medicine, Christian spiritual classics, and U.S. civics), our approach achieves 100% retrieval accuracy with sub-10ms latency, zero preprocessing, zero additional memory footprint, and zero infrastructure dependencies.