Is Grep All You Need? How Agent Harnesses Reshape Agentic Search (arxiv.org)

Recent research highlights the evolving capabilities of Large Language Model (LLM) agents in agentic workflows, specifically examining how retrieval strategies impact performance in AI-driven search systems. The study evaluates the effectiveness of grep versus vector retrieval strategies through two experiments. The first experiment utilized a custom agent harness, Chronos, alongside several native command-line interfaces (CLIs) to compare accuracy in answering queries, while the second experiment explored the influence of irrelevant surrounding text on retrieval performance. The findings reveal that grep generally outperforms vector retrieval in terms of accuracy, although the overall effectiveness varies significantly based on the choice of harness and tool-calling methods. This research is significant for the AI/ML community as it deepens the understanding of how different retrieval methods and architectural choices can enhance or hinder the capabilities of agentic search systems, potentially guiding future system designs towards more efficient AI interactions. As the industry continues to embrace retrieval-augmented generation, these insights will be critical in optimizing agent performance across diverse applications.