OrgAgent: Organize Your Multi-Agent System like a Company
While large language model-based multi-agent systems have shown strong potential for complex reasoning, how to effectively organize multiple agents remains an open question. In this paper, we introduce OrgAgent, a company-style hierarchical multi-agent framework that separates collaboration into governance, execution, and compliance layers. OrgAgent decomposes multi-agent reasoning into three layers: a governance layer for planning and resource allocation, an execution layer for task solving and
Record details
Published: 1 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
arXiv · 1 April 2026
LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
arXiv · 31 March 2026
APEX: Agent Payment Execution with Policy for Autonomous Agent API Access
arXiv · 2 April 2026
Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency
arXiv · 2 April 2026
Cognitive Comparability and the Limits of Governance: Evaluating Authority Under Radical Capability Asymmetry
arXiv · 3 April 2026
SentinelAgent: Intent-Verified Delegation Chains for Securing Federal Multi-Agent AI Systems
arXiv · 3 April 2026
How to cite this record
ethics.ai (1 April 2026), “OrgAgent: Organize Your Multi-Agent System like a Company,” evidence record 6490, https://ethics.ai/record/6490 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.