Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
This paper investigates whether simple grep-based search suffices in agentic tasks and how agent harness design outweighs model choice.
Echoing the famous Transformer paper, this work asks whether grep alone is sufficient for agentic search scenarios. The study focuses on 'agent harnesses'—the scaffolding wrapping an LLM, including prompting strategy, tool access, and memory—as the primary driver of search quality. Findings suggest harness design may matter more than the underlying model, challenging the community's focus on model scaling.
這篇發表於 arxiv 的研究論文(arXiv:2605.15184)標題刻意呼應 2017 年 Transformer 奠基之作「Attention Is All You Need」,以帶有挑釁意味的問句「Is Grep All You Need?」開場,引發讀者思考:在 AI Agent 執行搜尋任務時,最古老、最簡單的文字比對工具 grep 到底扮演什麼角色?
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