Automated Multi-Agent Organization Design: From Architecture Search to Organizational Learning

Authors: Shao-Jun Xia , Yuner Zhang , Huixin Zhang , Di Wang , Yifan Jiang , Liangxi Liu

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Abstract: Agentic system design is increasingly shifting from hand-crafted prompts and workflows toward automated search over agentic programs and interaction structures, with multi-agent systems receiving growing attention. Yet a central question remains unresolved: when does an automatically generated multi-agent structure exhibit a functionally meaningful division of responsibility? We argue that automated multi-agent design should move beyond searching for effective architectures toward identifying which responsibilities are functionally necessary, how they should be allocated, and when they should change. To explore this, we propose Automated Multi-Agent Organization Design (AMOD), which treats organization itself as a first-class design object, explicitly modeling how agent teams are composed, what functional responsibilities the agents assume, how they coordinate, and how collective decisions are made. We formulate AMOD as an execution-driven design loop. For each task, an automated designer instantiates an organization by assigning functional responsibilities and coordination rules to a set of agents. After execution, task outcomes, intermediate artifacts, and interaction traces are examined to identify which responsibilities may be missing, duplicated, or ineffective, and the organization is revised accordingly. We also propose testing discovered responsibilities by removing them, replacing them with generic roles, or changing their assignment under matched resource budgets, with complementary evaluations. AMOD provides a principled foundation for evidence-based and adaptive multi-agent organization design.

Comments:

First draft: 4 pages, 3 figures

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.27531 [cs.AI]

(or

arXiv:2610.27531v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.27531

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Huixin Zhang [ view email ]

[v1]

Tue, 20 Oct 2026 02:47:52 UTC (206 KB)

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