Workerville: Towards an Organizational Behavior Account of Agent Safety
Not on the map. Why?
Abstract (arXiv)
LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long-term memory. Existing safety research has examined these influences, but largely as separate agent components. How such factors jointly shape an agent's safety behavior from a unified perspective remains unmeasured. To bridge this gap, we advocate organizational behavior (OB) as a framework for studying the safety of advanced agents, reorganizing the objects of study, theoretical foundations, and experimental design around the relational structure in which agents operate. We present the first systematic formalization of counterproductive work behavior (CWB), a canonical safety-relevant subfield of OB, as Agentic Counterproductive Behavior (ACB). ACB specifies three organizational antecedents (vertical supervisor relations, horizontal peer norms, and internal cognitive structures) and maps them onto three counterproductive outcome dimensions (unauthorized disclosure, destructive operations, and production deviation). To operationalize ACB, we introduce Workerville, a controlled benchmark that manipulates organizational conditions over shared tasks, applying 16 organizational configurations to 210 tasks to yield 3,360 challenges, evaluated by human-validated agentic judges. Benchmarking 6 frontier LLMs, we find that (I) negative organizational antecedents exhibit non-monotonic amplification when combined, with the unauthorized-disclosure rate rising from 16.5% under no negative antecedent to 60.1% under two and falling back to 50.3% under three; (II) agents reproduce typical behavioral patterns predicted by human CWB research; (III) these results establish OB as a systematic framework for agent safety research, pointing toward a new research agenda.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 83 references that are in the map. First posted on arXiv 8 Oct 2026.
Builds on
28 of 83 references.
References · 28
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2025DeepSeek-V3.2: Pushing the Frontier of Open Large Language Modelscited by 755
- 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 350
- 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
- 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 119
- 2025AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agentscited by 82
- 2024Embodied LLM Agents Learn to Cooperate in Organized Teamscited by 81
- 2025OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safetycited by 71
- 2024S-Agents: Self-organizing Agents in Open-ended Environmentscited by 22
- 2024FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Modelscited by 21
16 more
- 2026A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecyclecited by 21
- 2024BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Modelscited by 17
- 2025Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systemscited by 15
- 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Codingcited by 11
- 2026AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompterscited by 7
- 2026Multi-User Large Language Model Agentscited by 6
- 2026When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundarycited by 6
- 2026BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Modelscited by 4
- 2026Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliancecited by 4
- 2026MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architecturescited by 3
- 2026OrgAgent: Organize Your Multi-Agent System like a Companycited by 2
- 2026Organizational Principles Enable Collective Intelligence in Embodied AIcited by 2
- 2026Can LLM Agents Sustain Long-Horizon Organizational Dynamics?cited by 1
- 2026AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoningcited by 1
- 2026Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordinationcited by 1
- 2026PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?cited by 1
Built on by
Not cited yet.