StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows
Score
78首次从市场验证的AI产品提取基准任务，弥补现有评测任务主观性问题。
研究方法系统、评估框架统一，任务设计与评分标准详细，但尚未披露具体任务案例与完整数据集。
为AI产品团队与Agent开发者提供真实需求导向的评测靶子，可直接用于能力诊断与迭代方向参考。
Agent应用正从Demo走向生产力，市场验证型基准切中当下关键评估痛点。
来自arXiv预印本，作者团队可追溯，但尚未经同行评审，数据与代码开放度未知。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
It innovatively anchors benchmark tasks on real market demand and directly reveals the capability gap of general-purpose agents in end-to-end work completion.
AI commentary
「What's most valuable here is swapping benchmark task sources from 'researcher guessing' to 'market selection'—a pragmatic shift. For agent product teams, the 30% completion rate is a high-value signal: stop chasing academic benchmarks; the moat lies in truly end-to-end deliverable workflows. Pay special attention to its task taxonomy and rubric design; if data is open-sourced, it can directly reveal your own model's weaknesses.」
Quick overview
Existing agent benchmarks mostly rely on researcher-selected tasks, so it remains unclear whether progress extends to work that real-world users actually demand. StartupBench systematically studies AI products with demonstrated adoption and extracts their workflows into deliverable-oriented end-to-end tasks with fine-grained rubrics. Under a unified agent harness, even the strongest model completes only about 30% of StartupBench, with complex instruction following and domain-specific expertise as major failure sources. This establishes an empirical measure of progress toward end-to-end completion of real-world user tasks.
- StartupBench derives real-user workflows from market-validated AI startup products instead of researcher-selected tasks.
- Tasks are deliverable-oriented and evaluated with fine-grained rubrics capturing complex requirements.
- Even the strongest model completes only about 30% under a unified agent harness, showing market-validated workflows remain beyond current general-purpose agents.
- Complex instruction following and domain-specific expertise are major failure sources.
Full text
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