AGENTIC-SYSTEMSApril 27, 20261 min read8 views

AgentSearchBench: A Benchmark for AI Agent Search in the Wild

AgentSearchBench is a large-scale benchmark featuring nearly 10,000 real-world agents, designed to address the challenge of identifying suitable AI agents for complex tasks. The study reveals that traditional text-based search methods are insufficient, highlighting the need for execution-grounded signals in agent discovery.

Computer Science > Artificial Intelligence

Title:AgentSearchBench: A Benchmark for AI Agent Search in the Wild

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.

Source: arXiv cs.AI Recent

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