AI Governance & QA Integration Framework. Integrated Framework for Compliance, QA Software, ISO Standards, AI Governance and LLM-based AI Agents

Abstract The AI Governance and QA Integration Framework (AGQIF) is a comprehensive, enterprise-grade reference architecture for the responsible deployment, governance, monitoring, and quality assurance of Artificial Intelligence systems based on Large Language Models (LLMs) and autonomous agents. The framework addresses a critical gap in current enterprise practice: the absence of a unified, operationally grounded governance model that integrates normative compliance (ISO/IEC 42001, ISO/IEC 23894, EU AI Act), software quality assurance, security controls, and agent orchestration within a single coherent structure. It is designed to be technology-agnostic, sector-independent, and applicable to any organisation deploying or planning to deploy LLM-based capabilities in production environments. Architectural scope. AGQIF defines eight interdependent architectural layers: Governance and Policy, Data and Knowledge, RAG (Retrieval-Augmented Generation) Pipeline, Chunking Strategy, Agent Orchestration, MCP (Model Context Protocol) Integration, Monitoring and Observability, and Audit and Compliance. Each layer has a defined responsibility boundary, standardised interfaces with adjacent layers, and independent governance and monitoring requirements. Operational content. The framework provides: 18 COBIT-style Control Objectives with normative cross-references, required actions, and evidence specifications; a 16-item KPI catalogue with measurement formulas, threshold targets, and review cadences; a three-tier SLA design guide with availability, latency, RTO, and RPO targets; a 10-item security threat catalogue mapped to the OWASP Top 10 for LLM Applications; a specialised AI testing agent architecture with a 10-test reliability suite; and detailed guidance on CLI integration and AI-assisted document generation (Printing Press pattern) within the MCP layer. Design principles. AGQIF is structured around three non-negotiable principles: Human-in-the-Loop (HITL) validation at all consequential decision gates; AI Augmentation rather than substitution of professional roles; and Operational Determinism through procedurally constrained, auditable agent workflows. Normative alignment. The framework aligns with ISO/IEC 42001:2023, ISO/IEC 23894:2023, ISO/IEC 25010:2023, ISO/IEC 27001:2022, ISO/IEC 27005:2022, NIST AI RMF 1.0, EU AI Act (Regulation EU 2024/1689), COBIT 2019, OWASP Top 10 for LLM Applications, and ITIL 4. Target audience. The framework is intended for AI governance professionals, enterprise architects, compliance and risk officers, QA engineers, and technology leaders responsible for the deployment of AI systems in regulated or high-stakes operational environments.