AI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture

A new arXiv paper proposes AI-GRACE (Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence), a use-case operationalization framework that connects organizational governance with technical implementation for agentic AI deployments. The framework assesses risks across seven proposed domains, derives requirements for pre-deployment assurance, runtime controls, and evidence, and introduces an Agent Operating Envelope for permitted actions and escalation conditions plus Risk-Aligned Independence Levels (RAIL) to summarize authorized independence. A fictional retail banking application illustrates the method, and the paper states empirical evaluation must still establish whether it improves deployment decisions, efficiency, and reuse.

arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe for a use case to deliver its intended outcome while meeting applicable obligations. This paper proposes AI-GRACE Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence as a use-case operationalization framework connecting organizational governance with technical implementation. The proposal draws on professional observations and a purposive synthesis of standards and literature, using design science to frame the method contribution and situational method engineering to guide contextual tailoring and reuse. The framework establishes objectives and obligations and then assesses risks in seven proposed domains, including mission and value realization. It derives requirements for assurance before deployment, runtime controls, and evidence, which guide capability qualification, gap assessment, and a logical architecture. An Agent Operating Envelope specifies permitted actions and escalation conditions, while Risk-Aligned Independence Levels RAIL summarize the authorized independence. A fictional retail banking application illustrates the method. The contribution is a traceable basis for deciding what an organization must implement, what it already supports, and what remains unresolved. Empirical evaluation must establish whether it improves deployment decisions, efficiency, and reuse.