2026· International Journal of AI, BigData, Computational and Management Studies· Vol 7, pp. 370-379· 0 citations
TL;DR
GUARD — Governance-Unified Agentic Runtime Detection and Response — extending the prior enterprise agentic AI lifecycle governance framework of Anuguthala (2026), providing the runtime enforcement layer that completes the governance-to-enforcement architecture initiated in the peer-reviewed prior governance framework.
Abstract
The rapid enterprise deployment of agentic artificial intelligence (AI) systems introduces operational risks that existing monitoring and incident response (IR) frameworks cannot address. Agentic systems exhibit non-deterministic behavior, autonomous tool invocation, dynamic reasoning chains, and emergent capabilities arising from multi-agent composition — properties that invalidate the static monitoring assumptions of DevOps, MLOps, and LLMOps paradigms. The National Institute of Standards and Technology (NIST AI 800-4, 2026) documents these gaps comprehensively, yet no validated runtime enforcement or IR framework exists for enterprise agentic AI. This paper presents GUARD — Governance-Unified Agentic Runtime Detection and Response — extending the prior enterprise agentic AI lifecycle governance framework of Anuguthala (2026), which established mandatory system-of-record registration, risk tiering, and governance checkpoints but did not specify runtime enforcement mechanisms or structured IR procedures. GUARD closes this gap through three primary contributions: (1) the Agentic System of Record (SoR), extended with a three-entity registration model covering individual agents, workflows, and inter-agent composition boundaries, serving as the authoritative runtime enforcement reference for all agent resource decisions; (2) Registry-Bound Execution Control (RBEC), a three-state runtime mechanism — allow, human-in-the-loop (HITL) pause, or kill-switch — validating every agent resource access against the SoR before execution; and (3) the Agentic Incident Response (AIR) lifecycle, a six-phase risk-tiered IR process anchored to the SoR. Two supporting contributions accompany these: a formal Lethal Trifecta boundary condition — adapted from the risk intersection concept articulated by Willison (2025) — operationalizing risk-tier enforcement within RBEC; and an empirical reference implementation on LangGraph evaluated across 100 trials per scenario. Empirical evaluation across seven scenarios confirms correct detection of all five violation categories — including Lethal Trifecta Boundary Breach detected through monitoring record correlation — with zero false positives across 100 trials per scenario, providing the runtime enforcement layer that completes the governance-to-enforcement architecture initiated in the peer-reviewed prior governance framework (Anuguthala, 2026).
Enterprise AI adoption has reached a structural inflection point: while a majority of organizations have deployed generative AI, few have established mature governance models for autonomous agents. This disparity reflects a fundamental architectural gap: Multi-Agent Systems, Enterprise Architecture, AI agent deployment, and software architecture have approached agent coordination from separate disciplinary perspectives, with no single framework integrating persistent memory, semantic interoperability, orchestration, human oversight, and normative enforcement. Following Design Science Research, this article develops the Agentic Enterprise Capability Framework (AECF), a five-layer architecture structured around Context Persistence (CPL), Semantic Interoperability (SIL), Hybrid Orchestration (HOL), Human Governance Interface (HGI), and Governance Envelope (GEL). The framework introduces the co-evolution constraint: technical capability layers cannot mature independently of governance capacity. This constraint is operationalized through Context-Enriched Pre-Execution Validation (CEPEV), which grounds compliance checks in operational memory. Five architectural propositions formalize inter-layer dependencies (P1), scalability boundaries (P2), governance effectiveness (P3), performance accumulation (P4), and a governance scaling law (P5). The study contributes an integrated architectural model, propositional formalization, and validation agenda for governed enterprise AI agent deployments.
Khammal Adil, Hamzane Ibrahim, Marzak Abdelaziz et al.· International Journal of Adv...· 0 citations
This paper argues that the introduction of agentic AI requires a substantial expansion of traditional enterprise architecture principles to address new behavioral, security, and governance risks emerging from non-deterministic AI systems interacting with heterogeneous operational platforms-ERP, HCM, CLM, asset management, workflow engines, and domain-specific applications.
Elizabeth Koumpan, Vimal Dimpi· AHFE International· 0 citations
LATTICE (Layered Agentic Triad Topology for Intelligent Coordinated Execution), a governance-first architecture that reframes the authorization question from “do the authors trust this AI?” to “do they trust this architecture?”
Elias Calboreanu· Frontiers in Artificial Inte...· 1 citation
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
Cloud ERP platforms have passed through three distinct automation eras. Scripted batch jobs gave way to robotic process automation, and RPA is now giving way to autonomous agentic AI — systems that reason over enterprise data, select tools dynamically, and execute multi-step business workflows without human direction at every step. The shift is not merely a capability upgrade. Agentic systems behave non-deterministically, invoke tools whose scope may exceed what static governance models anticipate, and can produce cascading process consequences in live financial environments. Governance frameworks built for predictive models and rule-based bots were not designed for this. This paper proposes the Agentic ERP Governance Framework (AEGF), a five-dimension instrument designed to guide the responsible deployment of autonomous AI agents in cloud-based industrial management systems. Drawing on Sociotechnical Systems Theory, the Technology-Organisation-Environment framework, and the NIST AI Risk Management Framework, the AEGF addresses Process Suitability, Autonomy Tiering, Governance and Auditability, Organisational Readiness, and Risk and Continuity Management as an integrated governance architecture. An application to accounts payable automation on Oracle ERP Cloud illustrates how the framework operates in a representative industrial management context.
Venkata Ramachandra Karthik Chundi· International journal of com...· 0 citations
Enterprise adoption of agentic artificial intelligence (AI) is moving from passive text generation toward autonomous planning, tool use and cross-system workflow execution. This transition creates a control gap: conventional model governance evaluates outputs or development processes, while agentic systems create risk through sequential actions, delegated authority and changing operating context. This paper develops a runtime assurance architecture (RAA) for enterprise agentic AI and formalizes a quantitative Autonomy-Risk Exposure (ARE) score for deciding when an agent may execute, must be sandboxed, requires human approval or must be blocked. A design-science method was used to synthesize requirements from AI risk-management standards, generative AI security guidance and agentic AI threat literature. The model was then evaluated through a reproducible scenario simulation of 2,000 enterprise agent episodes across knowledge assistance, data retrieval, internal workflow and external transaction tasks. Results show that the full RAA configuration reduced mean ARE from 35.7 to 24.4 points (31.5% reduction), decreased invalid or policy-conflicting actions from 9.8% to 4.6%, eliminated unsupervised pass-through of high-risk invalid actions in the simulated environment, and improved mean audit evidence coverage from 0.61 to 0.89. The control benefit was achieved with a mean latency overhead of 95 ms and human approval for 12.6% of episodes. The paper contributes a practical reference architecture, a risk-scoring equation, a policy decision algorithm and implementation guidance for organizations deploying agentic AI in regulated or high-consequence workflows.
K. Tan· World Journal of Advanced Re...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
