There’s an open question on whether grep is all you need for agentic search. This recent paper by @PwCUS (Sen et al.) seems to suggest that.…

There’s an open question on whether grep is all you need for agentic search. This recent paper by @PwCUS (Sen et al.) seems to suggest that. It’s titled “Is Grep All You Need? How Agent Harnesses Reshape Agentic Search”. They test various agentic harnesses (in-house, Claude Code, Codex), and equip the agent with both vector search and grep. They find that grep generally yields higher accuracy than semantic search. IMO the main gap of the paper is that it tests retrieval over conversational memory, not over a real-world corpus of enterprise documents. Standard enterprise RAG setups involve asking complex questions over a static document corpus (e.g. 10-Ks, legal contracts, SOPs). The corpus here is per-user chat history, which is quite a different document distribution. I do think that evolving agentic harnesses simplify the problem of retrieval - hence the popularity with file sandboxes and a vector db is “just a database” - but IMO there’s still more work to be done here. Paper: https://arxiv.org/pdf/2605.15184

Source: Jerry Liu (X) | 2026-05-17