JobBench: A New Benchmark for Human-Centric AI Agents
See why top models like Claude Opus struggle to reach 50% accuracy on real-world professional delegation tasks.
30-Second TL;DR
What Changed
Evaluates AI agents on 130 real-world professional tasks across 35 occupations.
Why It Matters
This benchmark challenges the industry to prioritize practical, complex professional assistance over generic capabilities. It provides a clearer roadmap for developers to build agents that effectively integrate into existing expert workflows.
What To Do Next
Review the JobBench dataset on arXiv to identify the specific professional workflows where your agentic models currently fail.
Key Points
- •Evaluates AI agents on 130 real-world professional tasks across 35 occupations.
- •Shifts focus from economic replacement to human-centric enhancement and delegation.
- •Current top-performing model, Claude Opus 4.7, achieves only 45.9% accuracy.
- •Uses fact-anchored chain-of-rubrics for precise performance grading.
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Original source: ArXiv AI ↗
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