Enterprise Validation and Governance of Knowledge-Grounded and Agentic AI Systems: Challenges, Solutions, and a Trustworthiness Assurance Framework

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.