AgentSearchBench: A Benchmark for AI Agent Search in the Wild

• URL: http://arxiv.org/abs/2604.22436v1
• Date: Fri, 24 Apr 2026 10:53:54 GMT
• Title: AgentSearchBench: A Benchmark for AI Agent Search in the Wild
• Abstract summary: We introduce AgentSearchBench, a large-scale benchmark for agent search in the wild.<n>The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high-level task descriptions.<n>We show that lightweight behavioral signals, including execution-aware probing, can substantially improve ranking quality.
• Score: 21.739444037726418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing research and benchmarks typically assume well-specified functionalities, controlled candidate pools, or only executable task queries, leaving realistic agent search scenarios insufficiently studied. We introduce AgentSearchBench, a large-scale benchmark for agent search in the wild, built from nearly 10,000 real-world agents across multiple providers. The benchmark formalizes agent search as retrieval and reranking problems under both executable task queries and high-level task descriptions, and evaluates relevance using execution-grounded performance signals. Experiments reveal a consistent gap between semantic similarity and actual agent performance, exposing the limitations of description-based retrieval and reranking methods. We further show that lightweight behavioral signals, including execution-aware probing, can substantially improve ranking quality, highlighting the importance of incorporating execution signals into agent discovery. Our code is available at https://github.com/Bingo-W/AgentSearchBench.

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