AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
New benchmark aggregates 40+ sources to create first unified system for recommending optimal AI agents for specific tasks.
A research team led by Yunxiao Shi has introduced AgentSelect, a groundbreaking benchmark that addresses the critical problem of selecting optimal AI agents from an exploding ecosystem of configurations. Unlike existing fragmented leaderboards that evaluate components in isolation, AgentSelect reframes agent selection as narrative query-to-agent recommendation, systematically converting heterogeneous evaluation artifacts from 40+ sources into unified interaction data. This creates the first reproducible infrastructure for studying how to match specific user queries with the best combination of backbone LLM (like Claude 3 or GPT-4) and specialized tools.
The benchmark comprises 111,179 queries, 107,721 deployable agents, and 251,103 interaction records spanning LLM-only, toolkit-only, and compositional agents. Analysis reveals a regime shift where traditional popularity-based recommendation methods fail, and content-aware capability matching becomes essential. The system shows that synthesized compositional interactions are learnable and improve coverage over realistic agent combinations. Models trained on AgentSelect successfully transfer to real-world platforms like MuleRun, demonstrating consistent gains on unseen agent catalogs and establishing a foundation for accelerating practical agent deployment.
- Aggregates 251,103 interaction records from 40+ sources covering 111,179 queries and 107,721 agents
- Shows traditional recommendation methods fail for agent selection, requiring content-aware capability matching
- Successfully transfers to real agent marketplaces like MuleRun with consistent performance gains
Why It Matters
Provides the first standardized way for enterprises to select optimal AI agent configurations for specific business tasks, moving beyond trial-and-error deployment.