AI-GRACE Framework Connects Organizational Obligations to Agentic AI Deployment

Summary

The paper proposes AI-GRACE, a use-case operationalization framework for organizations deploying agentic AI. Its central premise is that deployment decisions require more than judging whether a model is trustworthy: organizations must determine what to validate, control, and observe for a particular use case while meeting applicable obligations. Drawing on professional observations, a purposive synthesis of standards and literature, design science, and situational method engineering, the framework starts by establishing organizational objectives and obligations. It then assesses risks across seven proposed domains, including mission and value realization, and derives requirements for pre-deployment assurance, runtime controls, and evidence. These requirements support capability qualification, gap assessment, and the design of a logical architecture. The framework also introduces an Agent Operating Envelope to specify permitted actions and escalation conditions, while Risk-Aligned Independence Levels summarize how much authorized independence an agent may have. A fictional retail-banking application illustrates how the method can be applied. The claimed contribution is a traceable basis for deciding what an organization needs to implement, what it already supports, and what remains unresolved. The paper states that empirical evaluation is still needed to determine whether AI-GRACE improves deployment decisions, efficiency, and reuse.