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 empirical study organized into two experiments. Experiment 1 compares grep and vector retrieval on a 116-question sample from LongMemEval, using a custom agent harness (Chronos) and provider-native CLI harnesses (Claude Code, Codex, and Gemini CLI), for both inline tool results and file-based tool results that the model reads separately. Experiment 2 compares grep-only and vector-only retrieval while progressively mixing in additional unrelated conversation history, so that each query is embedded in more distracting material alongside the passages that matter. Across Chronos and the provider CLIs, grep generally yields higher accuracy than vector retrieval in our comparisons in experiment 1; at the same time, overall scores still depend strongly on which harness and tool-calling style is used, even when the underlying conversation data are the same.

翻译：大语言模型（LLM）智能代理的最新进展催生了复杂的代理式工作流，使模型能够自主检索信息、调用工具，并在大规模语料库中进行推理以代表用户完成任务。尽管检索增强生成（RAG）在代理式搜索系统中得到日益广泛的应用，现有文献仍缺乏关于检索策略选择如何与代理架构及工具调用范式相互影响的系统性比较。重要实践维度——包括工具输出如何呈现给模型、以及当搜索需应对更多不相关上下文文本时性能如何变化——在代理循环中仍未被充分探索。本文报告一项包含两个实验的实证研究。实验1在来自LongMemEval的116道题目样本上，比较grep与向量检索的性能，使用定制化代理框架（Chronos）及提供者原生CLI框架（Claude Code、Codex和Gemini CLI），涵盖内联工具结果和模型单独读取的基于文件的工具结果两种模式。实验2仅比较grep检索与向量检索，同时逐步混入额外无关对话历史，使每个查询嵌入更多干扰材料中。在Chronos与各提供者CLI中，实验1的比较显示grep通常比向量检索获得更高准确率；同时，即使底层对话数据相同，整体得分仍严重依赖于所使用的框架及工具调用风格。