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llms ai-agents retrieval-augmented-generation computational-linguistics

Is Grep All You Need? How Agent Harnesses Reshape Agentic Search

arxiv.org

June 9, 2026

2 min read

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53/100

Summary

Recent advancements in Large Language Model (LLM) agents allow for complex workflows where models autonomously retrieve information, utilize tools, and reason over large datasets. Retrieval-augmented generation (RAG) is increasingly adopted in agentic search systems to enhance task completion.

Key Takeaways

Grep retrieval generally yields higher accuracy than vector retrieval in agentic search systems when using a custom agent harness and provider-native CLI harnesses.
The performance of retrieval strategies is significantly influenced by the choice of agent architecture and tool-calling paradigm.
The study includes two experiments that assess the impact of irrelevant surrounding text on retrieval performance in agentic workflows.
Overall scores in retrieval tasks depend strongly on the specific harness and tool-calling style used, even with the same underlying conversation data.

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Community Sentiment

Mixed

Positives

Utilizing both regex and hybrid search methods can enhance retrieval performance, allowing agents to choose the best tool for specific tasks, which is a significant advancement.
The integration of local LLMs for summarization in hybrid search tools like qmd demonstrates the potential for improved contextual understanding in search applications.
Employing LLMs for query expansion can effectively increase the recall performance of retrieval algorithms, showcasing the synergy between traditional search methods and modern AI techniques.

Concerns

There are concerns that agents may not effectively determine which search method is superior, highlighting a potential gap in their training and decision-making capabilities.
The reliance on grep for searching in AI tools like Copilot raises questions about the effectiveness of using outdated methods instead of leveraging more advanced semantic databases.
The article's title may mislead readers into thinking that grep alone suffices for agentic search, while the reality is that a combination of methods is necessary for comprehensive understanding.