The CMA Agentic Platform: Autonomous Asset Verification and Algorithmic Auditor Governance

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a conceptual framework (Triadic Agentic Framework) for AI governance in audit, extending agency theory to autonomous systems; a foundation for empirical studies on AI auditor adoption.

🏢実務担当者:Presents a prototype for automating asset verification and ESG compliance, potentially reducing audit costs and enhancing transparency; relevant for firms exploring AI in assurance.

🏛政策担当者:Introduces Algorithmic Accountability as a regulatory domain and offers a blueprint for AI-driven supervisory monitoring; relevant for capital market authorities updating audit regulations.

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators.

• semanticscholar https://www.mdpi.com/2674-1032/5/2/55/pdf?version=1781701398first seen 2026-07-16 06:13:43