Abstract: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 workflows from the American Productivity & Quality Center (APQC) and, with a calibrated large language model (LLM), 13,417 Occupational Information Network (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.
From: Harang Ju [view email]
[v1]
Sat, 21 Feb 2026 00:55:09 UTC (393 KB)
[v2]
Sat, 18 Apr 2026 20:25:50 UTC (394 KB)
[v3]
Tue, 26 May 2026 18:00:03 UTC (405 KB)
[v4]
Sat, 25 Jul 2026 03:27:54 UTC (433 KB)
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