The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on automation. Yet, we lack a systematic understanding of the evolving landscape. In this paper, we address this gap by introducing a novel auditing framework to assess which occupational tasks workers want AI agents to automate or augment, and how those desires align with the current technological capabilities. Our framework features an audio-enhanced mini-interview to capture nuanced worker desires and introduces the Human Agency Scale (HAS) as a shared language to quantify the preferred level of human involvement. Using this framework, we construct the WORKBank database, building on the U.S. Department of Labor's O*NET database, to capture preferences from 1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation "Green Light" Zone, Automation "Red Light" Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics.

翻译：复合人工智能系统（亦称AI代理）的迅速崛起正在重塑劳动力市场，引发了关于岗位替代、人类能动性削弱以及自动化过度依赖的担忧。然而，我们尚缺乏对这一演变格局的系统性理解。本文通过引入一种新颖的审计框架来填补这一空白，该框架旨在评估劳动者希望AI代理自动化或增强哪些职业任务，以及这些期望如何与当前技术能力相匹配。我们的框架采用音频增强型微型访谈来捕捉劳动者细致入微的诉求，并引入“人类能动性量表”（HAS）作为量化人类期望参与程度的共同语言。基于美国劳工部O*NET数据库，我们运用该框架构建了WORKBank数据库，收录了涵盖104个职业、超过844项任务的1500名领域工作者的偏好数据及AI专家的能力评估。通过综合考量期望与技术能力，WORKBank中的任务被划分为四个区域：自动化“绿灯”区、自动化“红灯”区、研发机遇区及低优先级区。这揭示了AI代理开发的关键错配与潜在机遇。研究结果超越了简单的“是否自动化”二元划分，揭示了不同职业间多样化的HAS特征，反映出对人类参与程度的异质性期望。此外，我们的研究提供了早期信号，表明AI代理的整合可能如何重塑人类核心能力——从以信息处理为核心的技能转向人际交往技能。这些发现强调了使AI代理发展与人类期望相协调、并为劳动者适应不断变化的工作环境做好准备的重要性。