Loop-Back Authority in LLM Agent Teams: A Paired Experiment on Flat and Hierarchical Coordination
Does authority in AI teams improve the outcome? Organizational theory asserts that authority facilitates decision making, improving quality. Meanwhile, some nascent AI research suggests that revision under authority makes LLM output worse. Multi-agent LLM frameworks default to giving a Manager agent the authority to send a worker's output back for revision. Prior comparisons test the effect of authority using verifiable tasks. We conduct an experiment on an open-ended task, business-intelligence reporting, using a sample of 43 paired laptop products and 86 runs. Each report is written once by a hierarchical team and once by a flat team. We find that flat teams produce higher-quality repor...
| Authors | Burak Agachan, Max van Duijn, Amirhossein Zohrehvand |
|---|---|
| Primary category | cs.MA |
| Published | 13 September 2026 |
| Last updated at source | 7 October 2026 |
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