Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multi-agent AI systems, where coordination cost is directly measurable and can exceed the cost of the work itself. Distributed systems theory provides a precise criterion: coordination is required when a task specification is non-monotonic, meaning that as histories grow, new information can invalidate prior conclusions. Here we show that Thompson's classic taxonomy of interdependence maps to that criterion, yielding a decision rule for when coordination is required for correctness. We formalize the correspondence in a bridge theorem, apply the rule to 65 APQC workflows and (with a calibrated LLM) 13,417 O*NET tasks, and illustrate it in multi-agent AI simulations. Under our decompositions, 74% of workflows and 42% of O*NET tasks are monotonic, implying that up to 24-57% of coordination spending is unnecessary for correctness.

翻译：组织投入大量资源用于协调，但究竟哪些任务实际上需要协调才能保证正确性仍不明确。这一问题在多智能体AI系统中尤为突出，因为协调成本可直接度量，甚至可能超过任务本身的成本。分布式系统理论提供了一个精确判据：当任务规范非单调时（即随着历史增长，新信息可能推翻先前结论），协调就是必需的。本文证明汤普森经典的相互依赖分类法与此判据存在映射关系，从而得出判断协调何时为保持正确性所必需的决策规则。我们通过一座桥梁定理形式化这一对应关系，将该规则应用于65个APQC工作流及（借助经过校准的大语言模型）13,417个O*NET任务，并在多智能体AI模拟中加以验证。根据我们的分解方法，74%的工作流和42%的O*NET任务具有单调性，这意味着最高达24-57%的协调支出对保证正确性而言是不必要的。