The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems
- URL: http://arxiv.org/abs/2601.15059v1
- Date: Wed, 21 Jan 2026 15:05:27 GMT
- Title: The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems
- Abstract summary: We show modern CI/CD pipelines exhibit a structural failure in responsibility attribution.<n>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.<n>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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Software Engineering in the Agent Era From Trustworthy Change to Human Agent Software Organizations [4.180009544319738]
Software agents make digital execution elastic.<n>Problem framing, semantic commitment, verification, integration, attention, and residual-risk acceptance remain bounded by human cognition.
arXiv Detail & Related papers (2026-09-04T02:05:35Z) - Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels [53.63220028820581]
Embodied intelligence integrates learned perception and decision making with real-time computation, control, and physical interaction.<n>We define trustworthy embodied intelligence as the sustained capacity to execute specified tasks reliably.
arXiv Detail & Related papers (2026-07-28T17:50:44Z) - Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems [0.0]
This paper explores the architectural vulnerability of unbounded autonomy.<n>We instantiate this theory via the SMARt model, a four-layer framework featuring Stable, Meta-cognitive, Assisted, and Regulated states.<n>We conclude that formalizing failure management within the autonomy lifecycle is a crucial step toward realizing reliable and governed artificial intelligence.
arXiv Detail & Related papers (2026-05-26T19:49:23Z) - Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol [1.5755923640031846]
We analyze DeAI as a six-layer decentralizing stack.<n>We argue for a shift in the locus of governance from policy to protocol.
arXiv Detail & Related papers (2026-05-23T12:09:56Z) - Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems [2.124730017640531]
We introduce a verification framework for governed mutation systems that computes execution authority from structured artifacts.<n>DTF provides an infrastructure foundation for making agentic execution governable, auditable, and bounded in sovereign AI deployments.
arXiv Detail & Related papers (2026-05-13T17:58:52Z) - PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
arXiv Detail & Related papers (2026-05-12T04:59:47Z) - IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory [73.22944697933603]
Existing pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection.<n>We present IMPACT-Cycle, a supervisory multi-agent system that reformulates long-video understanding as a multimodal iterative claim-level maintenance.
arXiv Detail & Related papers (2026-04-22T03:03:33Z) - Governed Auditable Decisioning Under Uncertainty: Synthesis and Agentic Extension [0.0]
When automated decision systems fail, organizations often discover that formally compliant governance infrastructure cannot reconstruct what happened or why.<n>This paper synthesizes an operational governance evidence framework into an integrated chain and analytically assesses its transferability across four decision system architectures.
arXiv Detail & Related papers (2026-04-21T05:45:04Z) - PolicyBank: Evolving Policy Understanding for LLM Agents [51.86716874651299]
PolicyBank maintains structured, tool-level policy insights and iteratively refines them.<n>PolicyBank closes up to 82% of the gap toward a human oracle.
arXiv Detail & Related papers (2026-04-16T20:29:30Z) - Upholding Epistemic Agency: A Brouwerian Assertibility Constraint for Responsible AI [0.0]
I propose a Brouwer-inspired assertibility constraint for responsible AI.<n>In high-stakes domains, systems may assert or deny claims only if they can provide a publicly inspectable and contestable certificate of entitlement.
arXiv Detail & Related papers (2026-03-04T12:14:21Z) - Towards Selection as Power: Bounding Decision Authority in Autonomous Agents [0.0]
We propose a governance architecture that separates cognition, selection, and action into distinct domains and models autonomy as a vector of sovereignty.<n>We evaluate the system across multiple regulated financial scenarios under adversarial stress targeting variance manipulation, threshold gaming, framing skew, ordering effects, and entropy probing.<n>Results show that mechanical selection governance is implementable, auditable, and prevents deterministic outcome capture while preserving reasoning capacity.
arXiv Detail & Related papers (2026-02-16T10:10:47Z) - Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy [0.0]
This paper argues that cybersecurity orchestration should be reconceptualized as an agentic, multi-agent cognitive system.<n>We introduce a conceptual framework in which heterogeneous AI agents responsible for detection, hypothesis formation, contextual interpretation, explanation, and governance are coordinated through an explicit meta-cognitive judgement function.<n>Our contribution is to make this cognitive structure architecturally explicit and governable by embedding meta-cognitive judgement as a first-class system function.
arXiv Detail & Related papers (2026-02-12T12:52:49Z) - Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding [26.14684888478043]
This paper argues that prevailing approaches to transparency, user control, literacy, and governance do not define the foundational understanding humans must retain for oversight under sustained AI delegation.<n>To formalize this, we define the Cognitive Integrity Threshold (CIT) as the minimum comprehension required to preserve oversight, autonomy, and accountable participation under AI assistance.
arXiv Detail & Related papers (2026-01-31T18:37:33Z) - Agentic Uncertainty Quantification [76.94013626702183]
We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
arXiv Detail & Related papers (2026-01-22T07:16:26Z) - Logic-Driven Semantic Communication for Resilient Multi-Agent Systems [26.964933264412412]
6G networks are accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems.<n>This article proposes a formal definition of MAS resilience grounded in two complementary dimensions.<n>We design an agent architecture and develop decentralized algorithms to achieve both epistemic and action resilience.
arXiv Detail & Related papers (2026-01-11T00:54:09Z) - Assured Autonomy: How Operations Research Powers and Orchestrates Generative AI Systems [18.881800772626427]
We argue generative models can be fragile in operational domains unless paired with mechanisms that provide feasibility, robustness to distribution shift, and stress testing.<n>We develop a conceptual framework for assured autonomy grounded in operations research.<n>These elements define a research agenda for assured autonomy in safety-critical, reliability-sensitive operational domains.
arXiv Detail & Related papers (2025-12-30T04:24:06Z) - Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning [4.226647687395254]
This paper presents a Responsible(RAI) and Explainable(XAI) AI Agent Architecture for production-grade agentic based on multi-model consensus and reasoning-layer governance.<n>In the proposed design, a consortium of heterogeneous LLM and VLM agents independently generates candidate outputs from a shared input context.<n>A dedicated reasoning agent then performs structured consolidation across these outputs, enforcing safety and policy constraints, mitigating hallucinations and bias, and producing auditable, evidence-backed decisions.
arXiv Detail & Related papers (2025-12-25T14:49:25Z) - The Principle of Proportional Duty: A Knowledge-Duty Framework for Ethical Equilibrium in Human and Artificial Systems [0.0]
This paper introduces the Principle of Proportional Duty (PPD), a novel framework that models how ethical responsibility scales with an agent's epistemic state.<n>As uncertainty increases, Action Duty (the duty to act decisively) is proportionally converted into Repair Duty (the active duty to verify, inquire, and resolve uncertainty).<n>This paper applies the framework across four domains, clinical ethics, recipient-rights law, economic governance, and artificial intelligence, to demonstrate its cross-disciplinary validity.
arXiv Detail & Related papers (2025-12-07T02:37:07Z) - Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
arXiv Detail & Related papers (2025-11-10T22:24:21Z) - ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents [0.9740025522928777]
Large Language Models (LLMs) have demonstrated impressive capabilities, yet their deployment in high-stakes domains is hindered by inherent limitations in trustworthiness.<n>We introduce a generic neuro-symbolic approach, which we call Autonomous Trustworthy Agents (ATA)
arXiv Detail & Related papers (2025-10-18T07:35:54Z) - DecisionFlow: Advancing Large Language Model as Principled Decision Maker [49.088778182807395]
DecisionFlow is a novel decision modeling framework that guides models to reason over structured representations of actions, attributes, and constraints.<n>Rather than predicting answers directly from prompts, DecisionFlow builds a semantically grounded decision space and infers a latent utility function.<n> Empirical results show that DecisionFlow achieves up to 30% accuracy gains over strong prompting baselines.
arXiv Detail & Related papers (2025-05-27T16:23:53Z) - Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems.
Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
arXiv Detail & Related papers (2024-11-05T17:17:45Z) - Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework [54.40508478482667]
We present a comprehensive framework to disentangle, quantify, and mitigate uncertainty in perception and plan generation.<n>We propose methods tailored to the unique properties of perception and decision-making.<n>We show that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines.
arXiv Detail & Related papers (2024-11-03T17:32:00Z) - Accountability in Offline Reinforcement Learning: Explaining Decisions
with a Corpus of Examples [70.84093873437425]
This paper introduces the Accountable Offline Controller (AOC) that employs the offline dataset as the Decision Corpus.
AOC operates effectively in low-data scenarios, can be extended to the strictly offline imitation setting, and displays qualities of both conservation and adaptability.
We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high levels of performance while maintaining accountability.
arXiv Detail & Related papers (2023-10-11T17:20:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.