# Workerville: Towards an Organizational Behavior Account of Agent Safety arXiv: https://arxiv.org/abs/2610.11561 ยท HTML: https://mlsanity.com/paper/2610.11561/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. - Builds on 28 of its 83 references that are in the map. - First posted on arXiv 8 Oct 2026. ## Placed in - LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/) ## Builds on (most cited first, 28 of 28) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025) - [The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions](https://mlsanity.com/paper/2404.13208/) (2024) - [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023) - [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025) - [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023) - [AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents](https://mlsanity.com/paper/2506.00641/) (2025) - [Embodied LLM Agents Learn to Cooperate in Organized Teams](https://mlsanity.com/paper/2403.12482/) (2024) - [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://mlsanity.com/paper/2507.06134/) (2025) - [A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle](https://mlsanity.com/paper/2604.16548/) (2026) - [S-Agents: Self-organizing Agents in Open-ended Environments](https://mlsanity.com/paper/2402.04578/) (2024) - [FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Models](https://mlsanity.com/paper/2405.17814/) (2024) - [BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models](https://mlsanity.com/paper/2407.15240/) (2024) - [Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2505.21588/) (2025) - [CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding](https://mlsanity.com/paper/2512.04111/) (2025) - [AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompters](https://mlsanity.com/paper/2605.22645/) (2026) - [When Memory Becomes Authority: Benchmarking Authority Collapse at the Memory Consolidation Boundary](https://mlsanity.com/paper/2608.01679/) (2026) - [Multi-User Large Language Model Agents](https://mlsanity.com/paper/2604.08567/) (2026) - [BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models](https://mlsanity.com/paper/2604.11934/) (2026) - [MasDrift: Benchmarking Authorization Preservation Across Multi-Agent Architectures](https://mlsanity.com/paper/2608.07556/) (2026) - [Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance](https://mlsanity.com/paper/2608.12323/) (2026) - [OrgAgent: Organize Your Multi-Agent System like a Company](https://mlsanity.com/paper/2604.01020/) (2026) - [AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning](https://mlsanity.com/paper/2608.29988/) (2026) - [Organizational Principles Enable Collective Intelligence in Embodied AI](https://mlsanity.com/paper/2609.11737/) (2026) - [Can LLM Agents Sustain Long-Horizon Organizational Dynamics?](https://mlsanity.com/paper/2606.01199/) (2026) - [Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination](https://mlsanity.com/paper/2609.14767/) (2026) - [PACT: Can Enterprise AI Assistants Be Trusted Under Pressure?](https://mlsanity.com/paper/2609.18605/) (2026) ## 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.