mlsanityData to 7 Oct 2026

The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems

arXiv 2601.15059 ↗First posted 21 Jan 2026cs.AI cs.SY eess.SYcited by 3

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Abstract (arXiv)

Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 1 of its 10 references that are in the map. First posted on arXiv 21 Jan 2026.

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1 of 10 references.

References · 1

  1. 2026Semantic Laundering in AI Agent Architectures: Why Tool Boundaries Do Not Confer Epistemic Warrantcited by 4

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