Turning data into auditable action: Trust-by-design for agentic AI in finance, government, and corporate decision making

Abstract

Agentic artificial intelligence (AI) systems — capable of autonomous planning, sequential decision making and adaptive execution — represent a significant shift from traditional predictive models. While these systems offer substantial potential across financial services, government and corporate decision making, institutional adoption remains constrained by a persistent trust deficit driven by limited transparency, regulatory uncertainty and weak auditability. This paper presents a trust-by-design framework that positions the ‘action trace’ — a reconstructible record of decisions, tool interactions and policy checks — as the primary unit of assurance. The proposed Trust Stack architecture embeds auditability across five layers: data provenance, decision logic, action validation, continuous monitoring and human governance. Four quantitative trust metrics — decision consistency index, audit completeness score, human–AI alignment rate and rollback recovery time — operationalise system reliability and compliance readiness. The framework is demonstrated through detailed domain applications, including an end-to-end environment, social, and governance data-to-action case study, alongside use cases in asset management, government policy analysis and corporate strategy. The results show that audit-native design enables improved regulatory alignment, reduced compliance costs and increased stakeholder trust, providing a practical pathway for scaling agentic AI in high-stakes environments. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.