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.
This research paper published on arxiv (arXiv:2605.15184) deliberately echoes the 2017 Transformer founding work "Attention Is All You Need" with the provocative question, "Is Grep All You Need?" The opening prompts readers to ponder: What role does grep, the oldest and simplest text comparison tool, play when AI Agents perform search tasks?
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