Structural limits of decision attribution in modular automated governance systems

Modular automated governance systems increasingly coordinate distributed decision‐making across multiple agents. In blockchain‐based DAOs and automated on‐chain deployment pipelines, all state transitions are publicly observable. This transparency is frequently equated with accountability. We challenge this assumption. We argue that in certain modular governance architectures, full observability of execution does not guarantee univocal decision attribution. This limitation arises not from missing data or intentional privacy mechanisms, but from structural properties that separate deliberation, delegation, and execution without encoding deliberative provenance into the observable state. We frame this as a conditional non‐injectivity of the mapping between deliberative configurations and observable traces. Through case studies of real DAO governance executions, automated deployment pipelines, and controlled experiments on LLM‐based multi‐agent systems, we provide empirical evidence that distinct deliberative configurations can produce indistinguishable observable outcomes. We do not claim that attribution is universally impossible, but identify a structural regime in which transparency does not guarantee recoverability of deliberative control. We discuss the implications for accountability and safety in multi‐agent systems.