Governing the Agentic Frontier: Runtime Validation and Governance Frameworks for AI Driven Financial Workflows

Agentic AI is moving financial institutions beyond static models towards systems that can plan, retrieve information, invoke tools and act across multi-step workflows. Just as a trading desk is not governed by reviewing end-of-day profit and loss alone, an agentic system cannot be governed solely through its final response. Institutions must also determine whether each action remained within mandate, limits and approval requirements.

This paper presents a runtime governance approach for controlling the complete execution trajectory. Building on reusable capabilities, the Capability Transition System, governance-semantic telemetry, and tiered containment, it introduces TRACE, ARIA and Temporal Autonomy Risk to assess governance requirements, embed runtime controls and monitor accumulating autonomy exposure.

Using RegXplore – Crisil’s Regulatory Intelligence Platform as a practical implementation, the paper demonstrates how these concepts can be applied to an evidence-intensive financial workflow. It also assesses leading agentic frameworks and outlines the telemetry, policy, regulatory and operating-model capabilities required to move agentic AI from experimentation to scalable, resilient, and defensible enterprise-wide deployment.