arXiv:2605.26329v1 Announce Type: new Abstract: Current benchmarks for occupational AI agents are scoped primarily by economic values, telling a replacement story. We introduce JobBench, which evaluates AI agents on the workflows that experts identify as high-priority for delegation, empowering humans based on their needs instead of replacing them with GDP value. JobBench covers 130 agentic tasks across 35 occupations. Each task is packaged as a workspace of heterogeneous reference files, requiring the agent to reason through the cluttered information streams of real professional work. Outputs
The proliferation of increasingly capable AI agents necessitates new benchmarking standards that move beyond simple task replacement to consider human augmentation and complex workflow integration.
This benchmark shifts the narrative around AI agent deployment from pure economic displacement to human empowerment, vital for societal acceptance and effective integration of AI into professional roles.
The evaluation criteria for occupational AI agents are evolving to prioritize the delegation of high-priority tasks and integration into existing human workflows, rather than solely focusing on GDP value or replacement metrics.
- · AI agent developers focusing on human-in-the-loop systems
- · Professional services leveraging AI for augmentation
- · Knowledge workers seeking workflow optimization
- · Researchers developing human-centric AI evaluation methods
- · AI agent developers focused solely on cost reduction models
- · Benchmarks emphasizing simple task automation
- · Industries resistant to AI augmentation
JobBench will reorient AI agent research and development towards supporting and empowering human workers within complex professional environments.
Increased adoption of AI agents in white-collar professions as trust and demonstrable value based on human needs rather than just replacement grow.
A potential re-skilling surge as human workers learn to effectively collaborate with and manage advanced AI agents in their roles.
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Read at arXiv cs.AI