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arxiv:2601.15059

The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems

Published on Jan 21
· Submitted by
Rajkumar rawal
on Jan 22
Authors:
,

Abstract

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.

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Paper submitter

Some of the observations founded are :-

-- Authority capacity mismatch is structural :
Decisions are formally approved by humans, but the epistemic capacity to understand those decisions does not scale with agent generated throughput, creating a systematic gap between authority and understanding.

-- Responsibility vacuum emerges beyond a throughput threshold :
When decision generation rate exceeds bounded human verification capacity, personalized responsibility becomes unattainable even though processes are followed correctly .

-- Verification degrades into ritualized approval :
Human review persists as a formal act, but shifts from substantive inspection to reliance on proxy signals (e.g. CI green ), decoupling approval from understanding.

-- CI/CD automation amplifies the problem rather than solving it :
Adding more automated checks increases proxy signal density without restoring human capacity, accelerating cognitive offloading and widening the responsibility gap.

-- Local optimizations cannot eliminate the failure mode :
Better models, more CI, or improved tooling may shift thresholds but cannot remove the structural limit, only explicit redesign of responsibility boundaries (e.g. batch/system level ownership or constrained throughput) addresses the issue.

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