Aug 2026· International Journal of Integrative Studies (IJIS)· 0 citations
TL;DR
It is concluded that validation and governance of grounded and agentic AI must be treated as a first-class enterprise reliability engineering discipline — auditable, thresholddriven, and embedded across the inference lifecycle — rather than as an extension of conventional model evaluation.
Abstract
Enterprises are rapidly moving beyond static, single-turn Large Language Model (LLM) deployments toward knowledge-grounded systems — architectures such as Retrieval-Augmented Generation (RAG) that condition outputs on enterprise document stores — and toward agentic AI systems that plan, invoke external tools, and execute multi-step actions with limited human supervision. While prior work in enterprise AI assurance has addressed hallucination and demographic bias as discrete model-quality problems, the shift to grounded and agentic architectures introduces a qualitatively different class of governance risk: retrieval-grounding failure, tool-call malformation, permission-scope violation, and the compounding of small per-step errors into materially harmful multi-step outcomes. This paper characterizes these risks as structural properties of agentic enterprise systems rather than incidental model defects, and proposes the Agentic and Knowledge-Grounded Assurance (AKGA) framework, a six-layer governance pipeline that integrates grounding-fidelity verification, tool-call correctness checking, action-safety scoring, and a composite Agentic Risk Index (ARI) with tiered, threshold-based escalation to human review. Using a simulated benchmark spanning healthcare care-coordination, financial-services operations automation, and insurance claims-processing agents, the framework is shown to raise mean grounding fidelity, tool-call correctness, and action-safety scores from the 0.54–0.63 range to above 0.88 post-governance, while step-wise verification is shown to substantially suppress the compounding of risk across sequential agentic task chains relative to an ungoverned baseline. The paper concludes that validation and governance of grounded and agentic AI must be treated as a first-class enterprise reliability engineering discipline — auditable, thresholddriven, and embeddedacross the inference lifecycle — rather than as an extension of conventional model evaluation.
Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrate encoding how they decide, negotiate, and execute. Generic LLMs carry no firm-specific ontological priors; RAG remains brittle, with no path to executable action; static playbooks encode logic but cannot reason or adapt. This demands an architecture treating tacit-knowledge capture, ontological grounding, sovereign deployment, and auditable actuation as co-designed from the start. This paper introduces the Corporate Language Model (CLM), a framework transforming a firm's structured, unstructured, multimodal, and tacit knowledge into an ontology-grounded enterprise foundation upon which reasoning and governed execution are composed. CLM has five capability planes and four architectural pillars: a Neurosymbolic Mesh coupling generative models with a knowledge graph; a Skill Graph where reusable tactics, personas, objections, and goals are typed and composed; Living Digital Twins modeling functional areas as reasoning surrogates; and a Deep Security Layer enforcing sovereignty, traceability, and human oversight. A Spec-as-Code paradigm bridges grounded intent and executable artifact. CLM is one instantiation of this foundation-centric class. Four contributions follow: CLM is defined as a distinct object of study; the Skill Graph is introduced for compositional explainability by construction; the Wisdom Listener effect is proposed, whereby tacit-capable foundations compound in value with use, connecting to dynamic capabilities and organizational learning; and evidence from a JCI-accredited tertiary hospital in Brazil instantiates three of the six maturity stages under LGPD.
Enterprises are increasingly building agentic AI systems out of reusable skills — modular units that bundle prompts, reasoning strategies, tool integrations, and execution policies, and that get reused across many AI use cases. This pattern speeds up delivery, but it creates a risk that current AI governance frameworks were not designed for. A single privileged skill, reused across dozens of workflows, can quietly accumulate excess privilege, expand the operational blast radius of every workflow it touches, and drift from its original policy boundary. The NIST AI Risk Management Framework, ISO/IEC 42001, MITRE ATLAS, and OWASP's guidance for LLM and agentic applications all treat AI systems as a single object. None of them gives an organization a way to govern reusable skills as the cross-cutting assets they have become.
This paper argues that reusable agent skills should be treated as first-class governed enterprise assets, and that the enterprise agent harness — not the skill, the model, or the use case — must serve as the runtime trust boundary at which a skill's authority is granted. The paper proposes a runtime governance framework built on three constructs. Skill Risk Inheritance is a design-time model for reasoning about how risk flows through the composition of skills, tools, and use cases. Dynamic Capability Projection (DCP) is the runtime mechanism by which the harness grants, on each invocation, only the subset of a skill's declared capabilities authorized for the current use case and principal. Risk-Adaptive Capability Projection (RACP) extends DCP across time: the granted subset widens or narrows as runtime risk signals change. The framework is grounded in the object-capability tradition, modern policy engines such as OPA and Cedar, and Zero Trust architecture. It is validated through a prototype implementation on Open Policy Agent and a graph-based simulation, which together show that DCP reduces the runtime capability surface to 41% of declared scope withholding 59% of potential capabilities per invocation–at a median policy-evaluation overhead of 8.9ms, negligible against LLM inference latency. Critically, inheritance analysis revealed that 93% of simulated use cases operated at higher effective risk than their declared classification, a finding with immediate implications for enterprise AI risk programs.
Sandeep Kumar Anuguthala· International Journal for Sc...· 0 citations
The study develops a three-layer framework of agent-readability, traceability, and governability, theorizes agent-mediated contributions as governable boundary objects, and advances compliance-enabling digital innovation governance while preserving maintainer decision authority.
Enterprise software requires specification governance to transform probabilistic AI generation into deterministic, auditable engineering, and the SGRM framework is introduced, which defines four-component specification contracts, constrains stochastic generation via deterministic validation, and integrates generation, verification, and governance into a closed-loop architecture.
Large language models are increasingly used to review, clarify, rewrite, and trace software requirements. These applications create a governance problem that output-quality assessment alone cannot resolve: a fluent proposal may rely on inadmissible evidence, alter stakeholder intent, introduce unsupported specificity, or imply an organizational commitment that the model has no authority to make. Existing work on retrieval-augmented generation, controlled natural language, formal verification, human oversight, and AI governance supplies relevant controls, but it does not specify the procedural status of an individual LLM proposal relative to a controlled requirements artifact. This article develops an artifact-centered, human-in-the-loop framework in which the permitted influence of a proposal is the primary object of governance. The framework combines five governance functions—governed evidence, bounded context construction, controlled LLM analysis, pre-commit verification, and accountable human approval—with four artifact-influence states: A0 advisory observation, A1 evidence-linked candidate, A2 verified recommendation, and A3 approved and committed change. Its central theoretical claim is that output quality, evidential legitimacy, verification status, and authority to commit a change are distinct properties and should not be collapsed into a single confidence judgment. Seven falsifiable hypotheses translate the model into measurable comparisons involving source admissibility, context leakage, unsupported specificity, semantic drift, reviewer agreement, unreviewed changes, governance cost, and organizational maturity. Human review is treated as both a necessary decision boundary and a potential source of automation bias, anchoring, and fatigue. The framework is conceptual rather than empirically validated and provides a basis for controlled experiments, field studies, and longitudinal evaluation.
Chuanjin Zhu· Advances in Engineering Inno...· 0 citations
Much of the knowledge that keeps an industrial operation running is never written down: it sits with individual employees, scattered across incompatible systems, and cannot be found in time—a risk that becomes acute whenever people change roles or retire. Artificial intelligence (AI) is widely proposed as a remedy, but existing approaches concentrate on documentary, white-collar work; embodied, blue-collar work is comparatively underserved, particularly by large language models with no native grounding in physical activity. Existing work also treats AI as a single undifferentiated capability, leaving practitioners without a principled basis for choosing among technologies or integrating their outputs. This paper proposes a taxonomy of five AI paradigms (perceptive, dialogic, interpretive, structural and contextual), each defined by its contribution to one of three knowledge processes (capture, structuring, transfer) and by the type of work it serves, with a boundary marked where collective tacit knowledge resists codification. A separate orchestration layer, realised by autonomous agents and enabling technologies (knowledge graphs, retrieval-augmented generation, augmented reality), connects the paradigms into a pipeline. Applied diagnostically to three tools in one manufacturer’s training programme, an assembly-guidance system, a structured interview system, and a RAG-based conversational assistant, the taxonomy shows all three occupy the capture or transfer columns while structuring goes unserved, leaving each tool’s knowledge inaccessible to the others. A pump-assembly scenario shows how an agent-orchestrated pipeline over a shared knowledge graph, with human validation, could unify their outputs. The tools’ reported gains, a 29 per cent onboarding-time reduction and an over 90 per cent retrieval-time reduction for 3,000+ daily users, are taken at face value; whether integration compounds them, and for whom, is a working hypothesis, not a demonstrated result. The paper concludes with a staged evaluation strategy measuring cross-tool retrieval coverage and validation throughput to isolate the structural layer’s impact.
Mohamed Amine Guedria, Maximilian Dommermuth· European Conference on Knowl...· 0 citations
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