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This paper introduces the Intelligent Multi-Agent Collaboration System (IMACS), which decouples organizational roles, coordination methods, and collaboration algorithms in multi-agent frameworks using large language models (LLMs). By applying classic organizational theories in a configurable manner, the authors demonstrate that the choice of collaboration protocol can be learned and optimized through a contextual-bandit approach, leading to significant performance improvements. The study reveals that accountability placement critically influences outcomes, necessitating a tailored approach to organizational design based on the specific model used.
Adaptive Org Routing outperforms fixed collaboration protocols by dynamically selecting the best approach for each task, revealing that organizational design must be revalidated for each model family.
Multi-agent frameworks built on large language models (LLMs) routinely entangle three logically distinct concerns: who is on the team (organization), how members align (coordination), and which algorithm fuses their work (collaboration protocol). IMACS (Intelligent Multi-Agent Collaboration System) separates the three into orthogonal, independently swappable layers. Classic organizational theory (Belbin roles, Mintzberg coordination, RACI accountability) becomes executable, validated configuration, and the framework places six published collaboration algorithms behind a common interface while exposing roles, coordination, and accountability as independently configurable factors. We use this separation to conduct controlled comparisons in which organizational assignments vary while the collaboration protocol is held fixed. It also turns protocol choice into a variable that can be learned: Adaptive Org Routing, a contextual-bandit meta-protocol, selects a protocol per task under an explicit quality-cost tradeoff, outperforms every fixed protocol in a controlled study, and trains online on real benchmark and LLM-judge rewards. The ablations expose a mechanism. Accountability placement changes outcomes exactly when the protocol routes the deliverable through the accountable agent, and the winning placement flips across model families, so organizational design cannot be hard-coded; it must be revalidated, or learned, for each model binding.
