Is Grep All You Need? How Agent Harnesses Reshape Agentic Search
Recent advances in Large Language Model (LLM) agents have enabled complex agentic workflows where models autonomously retrieve information, call tools, and reason over large corpora to complete tasks on behalf of users. Despite the growing adoption of retrieval-augmented generation (RAG) in agentic search systems, existing literature lacks a systematic comparison of how retrieval strategy choice interacts with agent architecture and tool-calling paradigm. Important practical dimensions, including how tool outputs are presented to the model and how performance changes when searches must cope with more irrelevant surrounding text, remain under-explored in agent loops. This paper reports an ...
| Authors | Sahil Sen, Akhil Kasturi, Elias Lumer et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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