# Who Delegates to AI? Evidence from Agent Configurations in Github

arXiv: https://arxiv.org/abs/2608.20425 · HTML: https://mlsanity.com/paper/2608.20425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce](https://mlsanity.com/paper/2506.06576/) (2025)
- [Working with AI: Measuring the Applicability of Generative AI to Occupations](https://mlsanity.com/paper/2507.07935/) (2025)
- [The Adoption and Usage of AI Agents: Early Evidence from Perplexity](https://mlsanity.com/paper/2512.07828/) (2025)
- [Generative AI is already widespread in the public sector](https://mlsanity.com/paper/2401.01291/) (2024)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)
- [Follow the money: a startup-based measure of AI exposure across occupations, industries and regions](https://mlsanity.com/paper/2412.04924/) (2024)
- [The Iceberg Index: Measuring Skills-centered Exposure in the AI Economy](https://mlsanity.com/paper/2510.25137/) (2025)
- [Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption](https://mlsanity.com/paper/2604.00186/) (2026)
- [Critique of Agent Model](https://mlsanity.com/paper/2606.23991/) (2026)
- [AI-exposed jobs deteriorated before ChatGPT](https://mlsanity.com/paper/2601.02554/) (2026)
- [Jobs' AI Exposure Should Be Measured from Evidence, Not Model Priors](https://mlsanity.com/paper/2605.15474/) (2026)
- [Who Uses AI? Platform Selection and the Measurement of Occupational AI Exposure](https://mlsanity.com/paper/2605.21743/) (2026)
- [How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope](https://mlsanity.com/paper/2606.07489/) (2026)

## Abstract (arXiv)

A growing body of literature measures the extent to which occupations are exposed to AI, yet existing measures capture where AI could perform tasks rather than whether workers have actually adopted it. We introduce a distinct tier of exposure, delegated exposure, which records whether a worker has committed a task to AI by embedding it into a structured workflow. We operationalize this concept through the Agentic Adoption Index (AAI), measuring how closely an occupation's tasks align with the agentic routines that practitioners have built and shared. Using semantic embeddings of roughly 888,000 agent skill specifications from public GitHub repositories, we compute their similarity to nearly 18,000 O*NET task statements and aggregate these scores to the occupational level. We present three main findings. First, the occupations where task delegation concentrates differ sharply from those identified as most vulnerable by pre-AI automation frameworks. Second, the AAI aligns more closely with measures of technical capability than with measures of current conversational LLM use. Third, for occupations requiring a bachelor's degree or less, the AAI increases alongside average wage levels; however, this relationship reverses for occupations requiring a master's degree or higher, where adoption declines among higher earners. These patterns replicate on an independently collected corpus of agent skills from the Manus Skills Marketplace. This lower adoption among highly educated, high-earning workers may reflect tasks that inherently resist advance specification or professional discretion over the pacing of workflow codification. Distinguishing these mechanisms will require longitudinal measurement.