# Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce

arXiv: https://arxiv.org/abs/2506.06576 · HTML: https://mlsanity.com/paper/2506.06576/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https://mlsanity.com/paper/2303.10130/) (2023)
- [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://mlsanity.com/paper/2402.14207/) (2024)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration](https://mlsanity.com/paper/2412.15701/) (2024)
- [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025)
- [Into the Unknown Unknowns: Engaged Human Learning through Participation in Language Model Agent Conversations](https://mlsanity.com/paper/2408.15232/) (2024)
- [The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence](https://mlsanity.com/paper/2201.04200/) (2022)
- [AI Safety Should Prioritize the Future of Work](https://mlsanity.com/paper/2504.13959/) (2025)
- [Agents Are Not Enough](https://mlsanity.com/paper/2412.16241/) (2024)

## Built on by (most cited first, 30 of 38)

- [Working with AI: Measuring the Applicability of Generative AI to Occupations](https://mlsanity.com/paper/2507.07935/) (2025)
- [How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations](https://mlsanity.com/paper/2510.22780/) (2025)
- [Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification](https://mlsanity.com/paper/2510.11408/) (2025)
- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents](https://mlsanity.com/paper/2601.07264/) (2026)
- [When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation](https://mlsanity.com/paper/2604.00892/) (2026)
- [AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor](https://mlsanity.com/paper/2601.05752/) (2026)
- [How Well Does Agent Development Reflect Real-World Work?](https://mlsanity.com/paper/2603.01203/) (2026)
- [JobBench: Aligning Agent Work With Human Will](https://mlsanity.com/paper/2605.26329/) (2026)
- [LLMs Corrupt Your Documents When You Delegate](https://mlsanity.com/paper/2604.15597/) (2026)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [How are AI agents used? Evidence from 177,000 MCP tools](https://mlsanity.com/paper/2603.23802/) (2026)
- [Interaction as Intelligence: Deep Research With Human-AI Partnership](https://mlsanity.com/paper/2507.15759/) (2025)
- [Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld](https://mlsanity.com/paper/2508.09889/) (2025)
- [TheMCPCompany: Creating General-purpose Agents with Task-specific Tools](https://mlsanity.com/paper/2510.19286/) (2025)
- [Humans are Missing from AI Coding Agent Research](https://mlsanity.com/paper/2608.12355/) (2026)
- [Cloning a Conversational Voice AI Agent from Call,Recording Datasets for Telesales](https://mlsanity.com/paper/2509.04871/) (2025)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [Reflections and New Directions for Human-Centered Large Language Models](https://mlsanity.com/paper/2605.06901/) (2026)
- [CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration](https://mlsanity.com/paper/2509.11461/) (2025)
- [Auditing Student-AI Collaboration: A Case Study of Online Graduate CS Students](https://mlsanity.com/paper/2601.08697/) (2026)
- ["I Didn't Make the Micro Decisions": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration](https://mlsanity.com/paper/2605.21363/) (2026)
- [CollabSkill: Evaluating Human-Agent Collaboration On Real-World Tasks](https://mlsanity.com/paper/2606.09833/) (2026)
- [HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation](https://mlsanity.com/paper/2607.04329/) (2026)
- [CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks](https://mlsanity.com/paper/2608.18554/) (2026)
- [Idempotent Equilibrium Analysis of Hybrid Workflow Allocation: A Mathematical Schema for Future Work](https://mlsanity.com/paper/2508.01323/) (2025)
- [Economics of Human and AI Collaboration: When is Partial Automation More Attractive than Full Automation?](https://mlsanity.com/paper/2603.29121/) (2026)
- [A paradox of AI fluency](https://mlsanity.com/paper/2604.25905/) (2026)
- [Augment Engineering: A Methodology for Multi-Tool AI Orchestration Across Professional Domains](https://mlsanity.com/paper/2605.26146/) (2026)
- [Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation](https://mlsanity.com/paper/2605.30916/) (2026)

## Abstract (arXiv)

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.