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 overhead is directly measurable and routinely exceeds the cost of the work itself. However, distributed systems theory provides a precise answer: coordination is necessary if and only if a task is non-monotonic, meaning new information can invalidate prior conclusions. Here we show that a classic taxonomy of organizational interdependence maps onto the monotonicity criterion, yielding a decision rule and a measure of avoidable overhead (the Coordination Tax). Multi-agent simulations confirm both predictions. We classify 65 enterprise workflows and find that 48 (74%) are monotonic, then replicate on 13,417 occupational tasks from the O*NET database (42% monotonic). These classification rates imply that 24-57% of coordination spending is unnecessary for correctness.

翻译：组织投入大量资源用于协调，然而哪些任务真正需要协调才能确保正确性仍不明确。这一问题在多智能体AI系统中尤为突出，其中协调开销可直接测量且经常超过工作本身的成本。然而，分布式系统理论给出了精确答案：当且仅当任务具有非单调性时协调才是必要的——即新信息可能使先前结论失效。本文证明，经典的相互依赖组织分类法可映射到单调性标准，从而产生决策规则和可避免开销的度量指标（协调税）。多智能体模拟验证了这两项预测。我们对65个企业工作流进行分类，发现其中48个（74%）具有单调性，随后在O*NET数据库的13,417项职业任务中复现实验（42%具有单调性）。这些分类率表明，24-57%的协调支出对于确保正确性而言是不必要的。