WORKBank: Auditing AI Agent Readiness Against Worker Desire across the U.S. Workforce

The rapid rise of AI agents is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on automation. Yet these debates proceed without answering a fundamental question: which tasks should AI agents automate, and which present opportunities for augmentation? Existing approaches, from agent benchmarks to exposure studies, measure what agents can do but lack the evaluative apparatus to distinguish technical feasibility from social acceptability and what workers want. We address this gap by introducing a task-level auditing framework that contrasts worker desire with technological capability across the automation-augmentation spectrum. Our framework introduces the Human Agency Scale (HAS, H1-H5), a shared evaluation language that moves beyond binary automation framing to capture the full range of agent readiness. Instantiating this framework at scale, we construct WORKBank—comprising data from 1,500 domain workers across 104 occupations and capability assessments from 52 AI experts—as the first evaluation resource to jointly measure worker desire and AI readiness across the U.S. workforce. We demonstrate three evaluative use cases: auditing worker-centered automation demand across sectors, identifying mismatches between the desire-capability landscape and current AI research and investment, and informing collaborative agent design from HAS profiles. Critically, 31.6% of tasks fall in a high-capability but low-desire zone, while 12.7% represent high-desire but low-capability opportunities that current R&D underserves. All data, codebook, and the HAS specification are released publicly to support longitudinal tracking of AI agent readiness across the workforce.