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The Organizational Physics of Multi-Agent AI: Substrate-Independent Dysfunction in Autonomous Software Engineering Swarms

Authors: Jeremy McEntire;

The Organizational Physics of Multi-Agent AI: Substrate-Independent Dysfunction in Autonomous Software Engineering Swarms

Abstract

We present empirical evidence that organizational dysfunction is substrate-independent. In a controlled comparison, four coordination architectures-single agent, hierarchical, stigmergic (8 concurrent agents), and gated pipeline-built the same 7-service backend using the same LLM and $50 budget. Performance was inversely correlated with coordination complexity: 28/28, 18/28, 9/28, and 0/28. The pipeline consumed its entire budget on planning. The hierarchical coordinator refused to delegate. The stigmergic agents produced incompatible interfaces at every boundary. Only the single agent-with no coordination architecture-succeeded fully. In two additional studies, a pipeline swarm equipped with six explicit anti-dysfunction mechanisms produced the dysfunction those mechanisms were designed to prevent: bikeshedding (zero-factual-basis rejections), governance conflicts, backward pipeline oscillation, and verification theater. A contract-first alternative that replaces subjective evaluation with mechanical test verification narrowed the Goodhart gap but introduced its own dysfunction (specification perfectionism), suggesting that dysfunction migrates across architectures but does not disappear. We formalize these findings using Crawford-Sobel signal degradation, Goodhart's Law, and the Data Processing Inequality. The results are consistent with the hypothesis that coordination failure arises from information-theoretic constraints on any system coordinating through compressed representations, not from properties of the agents.

Keywords

coordination, organizational dysfunction, substrate independence, governance overhead, multi-agent AI, software engineering, information theory

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green