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，但哪些任务实际上为了正确性而需要协调仍不清楚。在多智能体AI系统中，这一问题尤为突出，因为协调成本可直接衡量，甚至可能超过工作本身的成本。分布式系统理论提供了一个精确的标准：当任务规范为非单调（即随着历史增长，新信息可能使先前的结论失效）时，需要协调。本文证明汤普森的经典相互依赖分类与此标准相对应，从而得出一个何时因正确性需要协调的决策规则。我们通过一个桥梁定理形式化这种对应关系，将该规则应用于65个APQC工作流和（借助校准后的LLM）13,417个O*NET任务，并在多智能体AI模拟中加以说明。根据我们的分解，74%的工作流和42%的O*NET任务是单调的，这意味着高达24-57%的协调支出对于正确性而言是不必要的。