Organizational Theory as the Missing Foundation for Agentic AI Systems

Abstract: As artificial intelligence systems evolve toward multi-agent architectures, practitioners and researchers are rediscovering coordination challenges that organizational theorists have studied for decades. Current agentic AI implementations often assume models possess unlimited managerial capacity, ignore well-established principles of span of control, and rely on unstructured information transfer between agents. This creates predictable coordination failures, token inefficiency, and brittleness at scale. Drawing on organizational theory—particularly concepts of span of control, boundary objects, coupling mechanisms, and bounded rationality—this article argues that effective multi-agent AI systems require deliberate organizational design choices, not merely more capable models. Through examination of early implementations and organizational parallels, we identify evidence-based design principles including hierarchical structuring with intermediate coordination layers, structured artifacts for inter-agent communication, and calibrated coupling mechanisms. Organizations that approach agentic AI as an organizational design challenge rather than purely a technical one will achieve more reliable, scalable, and economically viable systems. The article concludes by proposing experimental directions that integrate organizational science with AI system design.

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