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:2607. 01510v1 Announce Type: new Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should they play?
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
The paper "Value-Preserving Architectures for Agentic AI Systems" discusses how architectural choices in large language model-based multi‑agent systems (MAS) can promote human‑centered values such as privacy, fairness, and safety. It introduces three value‑preserving architectural patterns: a privacy‑aware federated topology, a distributed architecture that encourages pluralism and diversity, and a guard‑agent design to detect and mitigate unfairness. Representative use cases illustrate how these patterns can be applied in real‑world scenarios, aiming to provide guidelines for building trustworthy MAS.
arXiv:2606. 13608v1 Announce Type: new Abstract: Agent systems are advancing quickly across domains, but their evaluation remains fragmented.
arXiv:2606. 04321v1 Announce Type: new Abstract: Agentic AI deployments face a recurring design tension: heavy human oversight limits scale, while broad autonomy outruns accountability.
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
arXiv:2607. 18257v1 Announce Type: cross Abstract: When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose action space they cannot fully anticipate.
arXiv:2606. 11007v1 Announce Type: cross Abstract: OpenClaw has rapidly emerged as a transformative artificial intelligence (AI) agent framework, and its ability to autonomously execute complex, multi-step tasks has attracted an ever-growing and diverse user base.
arXiv:2509.08494v2 Announce Type: replace-cross Abstract: As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures....
The article "Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks" surveys the lack of a standard definition for AI agents and organizes this ambiguity into five dimensions: environmental interaction, learning and adaptation, autonomy, goal‑directed behavior, and temporal coherence. It reviews how each dimension has been conceptualized in prior work and compiles the metrics, benchmarks, and evaluation frameworks used to assess them. The authors also introduce the Agent Compendium, a public digital resource that extends these evaluation methods, aiming to provide a common structure for evaluating and comparing agent capabilities across AI systems.
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
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.