arXiv:2603. 02961v2 Announce Type: replace-cross Abstract: As AI systems enter institutional workflows, workers must decide whether to delegate task execution to AI and how much effort to invest in verifying AI outputs, while institutions evaluate workers using outcome-based standards that may misalign with workers' private costs.
arXiv:2606. 15563v1 Announce Type: new Abstract: AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers.
arXiv:2602. 13213v2 Announce Type: replace Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing.
The paper introduces the Agentic Adoption Index (AAI), a new metric that captures whether workers actually delegate tasks to AI within their workflows, rather than merely measuring potential AI applicability. Using 53,000 agent skill specifications and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those previously deemed most at risk, that AAI aligns more closely with AI’s capabilities than current usage, and that adoption peaks at mid‑wage, bachelor’s‑level occupations while declining at both ends of the wage and education spectrum. The study highlights that technical availability explains much of the variation, but other factors—such as resistance to specification or professional discretion—also influence who adopts AI. whyItMatters":"The findings suggest that actual AI adoption patterns differ from prior risk assessments, indicating that factors beyond technical feasibility shape who delegates to AI, which has implications for workforce planning and policy."
arXiv:2606. 12430v1 Announce Type: cross Abstract: Some claim that AI agents will free workers from the boring parts of their jobs, yet little is known about how workers themselves identify which tasks should be automated.
The paper introduces the Agentic Adoption Index (AAI), a new measure of delegated exposure that captures whether workers actually commit tasks to AI within structured workflows. Using semantic embeddings of 888,000 agent skill specifications from GitHub and 18,000 O*NET task statements, the authors find that occupations with high delegation differ from those most vulnerable to pre-AI automation, that AAI correlates more with technical capability than with current LLM use, and that for lower‑educated occupations AAI rises with wages while it falls for higher‑educated, high‑earning workers. These patterns also appear in an independent corpus from the Manus Skills Marketplace.
The paper examines how users delegate tasks to the AI agent OpenClaw by analyzing 73,093 Reddit posts. It identifies 21 human values grouped into six categories—such as Autonomous Operation, Dependable Operation, Affordable Operation, Bounded Reach, Reviewability, and Equitable Access—and finds that values are largely satisfied when users describe the agent’s outputs but often unmet when users discuss supervising the agent. The authors term this pattern "value‑sensitive delegation," emphasizing that supporting human values requires attention to both what an agent does and the conditions users set around its use.
arXiv:2608. 07556v1 Announce Type: cross Abstract: Multi-agent systems (MAS) decompose long-horizon tasks across supervisors and subagents, but delegated goals do not necessarily carry their original authorization boundaries.
arXiv:2606. 17099v1 Announce Type: cross Abstract: AI coding agents increasingly accept assigned software tasks, modify repositories under bounded authority, and return work packages for review.
arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
The paper introduces a method for safely retiring procedural guidance in AI agents that control physical actions. It proposes matched authority counterfactuals and a two‑gate retirement certificate to ensure that reductions preserve authorized utility while eliminating unauthorized protected effects. Experiments across multiple models and skill bundles show that task‑certified reductions can remove most skill clauses, but only a combined protocol passes both utility and safety gates in all tested configurations.
arXiv:2603. 27049v2 Announce Type: replace-cross Abstract: AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved.
arXiv:2607. 21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists.