Explore/agent app/Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClaw
V

Renkai Ma, Ruyuan Wan, Xuan Lu, Fan Yang, Chen Chen, Lingyao Li/Value-Sensitive Delegation in Everyday AI Agent Use: Evidence from OpenClawUnknown

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 OpenClaw, 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.

agent app
GitHubCompare
Refreshed 7h ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed7h ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 0 days observed0 not enough history
snapshots
not enough history
Repository activity · 0 days observednot enough history from pushed_at
inactivepushed
not enough history
not enough history
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

Alternatives

O
OccuReward: LLM-Guided Occupant-Centric Reward Shaping for Demographic Equity in Grid-Interactive Buildings
0 · agent app
55
C
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
0 · agent app
55
U
Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents
0 · agent app
55
S
SysEvolve: An AI-native, safe, autonomous adversarial attack-defense co-evolutionary system
0 · agent app
55Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler7h ago
Monitor for new releasesongoing