Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw

Users increasingly delegate work to autonomous AI agents, yet evaluations typically measure task completion rather than the values users prioritize. Using Value Sensitive Design, we analyzed, with LLM assistance, 73,093 first-person Reddit posts about using Open- Claw, each for its human value, agent aspect, value fulfillment, and user outcome. The 21 values form six value groups, including Autonomous, Dependable, and Affordable Operation, Bounded Reach, Reviewability, and Equitable Access. Relative to each aspect’s corpus share, values clustered not at the agent’s outputs but at the operating conditions users set around a run. Values were usually met where users described what the agent delivered, in five of six groups, and mostly unmet where users described supervising it, in all six groups. We conceptualize this pattern as value-sensitive delegation. Supporting human values requires attention not only to what an agent accomplishes, but to the conditions users set around delegation, including cost, access, and oversight.

Introduction. An autonomous agent is an AI system that independently executes tasks and manages workflows on behalf of a user. Users increasingly delegate real work to these agents1. OpenClaw [8],2 an open-source AI agent that runs on a user’s own computer and performs tasks through chat apps such as WhatsApp, exemplifies this class of system. Released in November 2025, it had drawn more than 370,000 GitHub stars by August 2026 [40]. Such an agent differs from a typical conversational assistant in where execution occurs and when a user can review it [21, 68]. A typical conversational assistant asked to fix a bug returns a patch for the user to inspect, whereas an agent reads the repository, edits the files, runs the tests, and commits the result under the user’s credentials, with few checkpoints in between [12, 38]. We call one such stretch of delegated work, from the user’s instruction to the outcome the user reads afterward, a run.

Discussion / Conclusion. While prior work evaluates agents by task completion [28, 34, 64] or scores value-relevant judgments [30, 59], our study is among the first to trace, using OpenClaw as an exemplar agent, which human values users invoke in everyday agent use and which agent aspects those values attach to. Below, we describe this relationship as value-sensitive delegation and ground it in VSD’s interactional position (Section 6.1), examine the gains and costs users attributed to running OpenClaw (Section 6.2), and translate both into design implications (Section 6.3). Value Sensitive Design (VSD) treats human values as interactional, meaning that a value takes shape in the relationship between a technology’s properties, the people affected by it, and the context where it is used [17]. A delegated AI agent like OpenClaw stretches that relationship over time, because the user configures the agent, the task runs autonomously, and the user checks the outcomes afterward. Section 4.2 reports that in four of the six value groups, user posts concentrated on agent aspects the user configures rather than on agent aspects the agent exercises without the user.