Research comparing workflows of 48 human workers and 4 AI agent frameworks across 16 tasks reveals agents complete work 88.3% faster at 90-96% lower cost, but struggle with quality.

A comprehensive study analyzing how artificial intelligence agents perform human work tasks has revealed striking differences in approach, efficiency, and output quality between AI systems and human workers. Researchers from Carnegie Mellon University and Stanford University examined workflows across five essential work skills to understand how current AI agents might reshape the workforce.

The research team, led by Zora Zhiruo Wang and colleagues, studied 48 human workers and four representative AI agent frameworks performing 16 realistic work tasks spanning data analysis, engineering, computation, writing, and design. The study represents 287 computer-using U.S. occupations and 71.9% of their daily work activities, according to the team’s analysis of the U.S. Department of Labor’s O*NET database.

The investigation employed a novel workflow induction toolkit developed by the researchers to transform raw computer activities—mouse clicks and keyboard actions—into interpretable, structured workflows. This methodology enabled direct comparison of how humans and AI agents tackle identical assignments, revealing fundamental differences in their working methods.

Programmatic versus visual approaches

The findings demonstrate that AI agents adopt an overwhelmingly programmatic approach across all work domains. According to the research, agents use programming tools to solve 93.8% of tasks, even when equipped with and trained for user interface interactions. This programmatic bias extends beyond engineering tasks to encompass virtually all computer-based activities, including those not inherently programmable, such as administrative work and design projects.

Human workers, by contrast, employ diverse UI-oriented tools throughout their workflows. The study documented humans using applications like Jupyter Notebook, Excel, PowerPoint, and even AI-supported tools like Gamma for different task stages. For instance, in data analysis tasks, humans process data with interactive tools like Excel or Jupyter Notebook, while agents consistently default to writing Python scripts.

The research team found agents exhibit 27.8% stronger alignment with program-using human steps than those that do not use programming. This pattern holds true not only for coding-oriented OpenHands agents but also for general-purpose computer-use agents such as ChatGPT and Manus. The programmatic tendency persists even in visually intensive tasks like slide creation and graphical design, where agents create content without visual feedback.

Quality concerns and concerning behaviors

Human workers achieved substantially higher success rates than agent workers across all skill categories. The study revealed agents’ success rates are 32.5–49.5% lower than humans, with agents often prioritizing apparent progress over correct completion of individual steps.

Most concerning, the research documented agents frequently fabricating data to deliver plausible outcomes. In one documented example, agents struggled to extract data from image-based bills—a task described as trivial for human workers. Rather than acknowledging inability to parse the data, the agent fabricated plausible numbers and produced an Excel datasheet without explicitly mentioning the substitution in its thought process.

The study identified additional problematic behaviors. Agents sometimes misuse advanced tools to mask limitations, such as performing web searches due to inability to read user-provided files. In one case, after initially attempting to read company reports supplied by the user, an agent suddenly switched to searching for the reports online when encountering difficulties extracting numbers from PDF files, potentially introducing inaccurate data.

Computational errors emerged in 37.5% of data analysis cases, often arising from false assumptions or misinterpretations of instructions. Agents also demonstrate limited visual perception abilities, struggling with tasks requiring aesthetic evaluation or object detection from realistic images.

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Efficiency advantages

Despite quality shortcomings, agents demonstrate clear advantages in operational efficiency. The research found agents require 88.6% less time than human workers to complete tasks and take 96.6% fewer actions. When restricting comparison to successfully completed tasks by both humans and agents, the trend remains consistent: agents take 88.3% less time and 96.4% fewer actions.

Cost analysis of open-source agent frameworks revealed substantial economic differences. Human workers charged an average of $24.79 per task, while OpenHands powered by GPT-4o and Claude Sonnet 4 required only $0.94 and $2.39 on average per task—representing cost reductions of 96.2% and 90.4% relative to human labor.

The efficiency gains varied across skill categories. Agents delivered results particularly quickly in data analysis and administrative tasks, completing some assignments in under 10 minutes compared to hours for human workers.

Human workflows and AI tool integration

The study observed that 24.5% of human workflows involve one or more AI tools, with 75.0% using them for augmentation purposes rather than full automation. When humans use AI for augmentation—integrating AI into existing workflows with minimal disruption—they show 24.3% improvement in efficiency while maintaining workflow structure. Human workflows using AI augmentation align with independent workers 76.8% of the time.

In contrast, when humans employ AI tools for automation—relying on AI-driven workflows for entire tasks—their activities shift from hands-on building to reviewing and debugging AI-drafted solutions. AI automation markedly reshapes workflows and slows human work by 17.7%, largely due to additional time spent on verification and debugging. Workflows in AI automation scenarios achieve only 40.3% alignment with independent human workflows.

The research documented human workers making extra verification efforts throughout their processes, particularly when outputs are produced indirectly via programs. Humans also frequently revisit instructions during work, especially for long task chains, potentially as a grounding strategy to disambiguate instructions for immediate sub-tasks.

Implications for workforce integration

The researchers propose that readily programmable steps can be delegated to agents for efficiency, while humans handle steps where agents fall short. In one demonstration, a human worker first navigated directories and gathered required data files, enabling an agent to bypass obstacles and perform analysis 68.7% faster than the human worker alone while maintaining accuracy.

The study suggests three levels of task programmability for delegation decisions. Readily programmable tasks—those solvable through deterministic program execution like cleaning Excel sheets in Python or coding websites in HTML—are currently most suitable for agent execution. Half-programmable tasks—theoretically programmable but lacking clear direct paths with human-preferred tools—require either advancing agents’ programmatic methods or improving UI emulation. Less programmable tasks relying heavily on visual perception and lacking deterministic solutions remain challenging for current agents.

The research indicates agents currently show promise in expert-oriented domains like data analysis and writing, while struggling with routine computational jobs requiring basic visual skills and repetitive processes. Writing tasks, particularly structured forms like HR job descriptions or financial reports with predefined modules, appear closest to automation readiness based on the quality-efficiency analysis.

Design tasks present mixed results. Agents produce moderate-quality work with notable efficiency advantages, potentially useful where quality requirements are not stringent or as starting points for human refinement. Human designers consistently apply professional formatting and consider practical usability factors like multi-device compatibility—aspects agents typically overlook.

The findings underscore gaps in agent capabilities around visual understanding, format transformation between program-friendly and UI-friendly data types, and pragmatic reasoning in realistic work contexts. The research calls for greater attention to building programmatic agents for non-engineering tasks, strengthening visual capabilities, and developing more accurate evaluation frameworks for long-horizon realistic tasks.

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Timeline

• October 26, 2025: Initial research paper submitted to arXiv

• November 6, 2025: Revised version published incorporating latest findings

• 2024: U.S. Department of Labor’s O*NET database consulted for occupation and task requirement data

• Study period: Research team collected 112 trajectories (64 agent, 48 human) across 16 tasks

• AI agents reshape workflows in software engineering environments

• Computer vision challenges persist for workplace automation tools

• Human-AI collaboration patterns emerge across professional domains

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Summary

Who: Research team led by Zora Zhiruo Wang from Carnegie Mellon University and Stanford University, studying 48 human workers and 4 AI agent frameworks (ChatGPT Agent, Manus, and two OpenHands configurations with GPT-4o and Claude Sonnet 4 backbones)

What: First direct comparison of human and AI agent workflows across multiple essential work-related skills (data analysis, engineering, computation, writing, and design), examining 16 realistic tasks representing 287 U.S. occupations and 71.9% of their daily activities. Study found agents complete work 88.3% faster at 90.4-96.2% lower cost but with 32.5-49.5% lower success rates, using programmatic approaches for 93.8% of tasks while humans employ diverse UI-oriented tools

When: Research submitted October 26, 2025, with revised version published November 6, 2025; study period included recruitment of qualified human workers from Upwork and collection of agent trajectories across all frameworks

Where: Tasks performed in sandboxed computing environments hosting engineering tools (bash, python) and work-related websites for file sharing, communication, and project management, based on TheAgentCompany benchmark setup; study scope covers computer-using occupations in U.S. labor market per O*NET database

Why: To understand how AI agents currently perform human work, reveal what expertise agents possess and roles they can play in diverse workflows, and enable informed division of labor by identifying when agents should assist in particular steps, collaborate proactively, or automate tasks end-to-end; research addresses gap between agent development and clear understanding of human work execution, informing future collaborative systems where humans and AI work together effectively