Automation’s Early Footprint

Researchers created the Agentic Task Exposure (ATE) dataset by analyzing 696,291 publicly listed Model Context Protocol tools from 123,069 servers to identify tasks that developers have judged ready for end-to-end automation. Only 2.6% of tools matched an existing O*NET occupational task under a strict standard, a likely lower bound because private enterprise tools were excluded, and these matches were concentrated in software-oriented occupations such as graphic design, while 419 of 923 occupations showed no agentic tool activity. Most unmatched tools represented narrower substeps of recognized work, multi-task workflows, or infrastructure for operating agents, with only about 3% of categories appearing to describe genuinely new work, largely involving the management and evaluation of AI agents. Tool availability correlated substantially with theoretical AI exposure across occupations, but neither exposure nor the amount of tooling predicted whether automation targeted routine or specialized tasks. The analysis found contrasting patterns: healthcare and computing tools more often reached specialized work, potentially lowering expertise requirements for remaining human work, whereas legal, production, and sales tools tended to automate routine edges and leave specialized work to people. The authors emphasize that ATE measures available supply rather than adoption or labor-market effects, and argue that tracking where automation lands within jobs is essential for understanding potential consequences for wages, employment, skill development, and entry-level career pathways.

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