Governance-Controlled Treasury Automation

The convergence of tokenized finance and agentic artificial intelligence enables a new generation of treasury automation systems capable of optimizing liquidity, collateral, settlement flows, and cross-border fund allocation in near real time. However, these capabilities also create a critical governance challenge: autonomous systems may obtain practical authority over financial assets without independently trusted control boundaries.

Building on previous work introducing Policy Roots of Trust, Trusted Governance Architectures for Agentic AI, and Security Governance Filters, this paper presents a sector-specific instantiation for regulated treasury environments. In the proposed model, AI systems generate treasury action proposals, while independently trusted governance components evaluate enterprise policies before execution.

We focus on a specific governance objective among the broader capabilities of prior architectures: enforcement of enterprise treasury policies controlling authorized movement of funds, segregation of duties, jurisdictional restrictions, exposure limits, and escalation workflows.

A representative multinational intraday liquidity use case involving tokenized and traditional payment rails is analyzed. The paper shows how governance-controlled execution can preserve automation benefits while maintaining bank-grade control, auditability, and regulatory alignment.