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

The AI-GRACE framework is proposed to operationalize agentic artificial intelligence (AI) deployments, linking organizational governance with technical implementation. It addresses the critical need for organizations to establish clear validation, control, and observation mechanisms for AI use cases, ensuring they meet intended outcomes and comply with obligations beyond merely assessing model trustworthiness. The framework is derived from professional observations and a synthesis of existing standards and literature.

Why it matters

The rapid advancement and deployment of agentic AI necessitate robust frameworks to manage associated risks and ensure ethical, compliant, and effective integration into operations. This framework provides a structured approach for organizations to move beyond basic model trustworthiness assessments, enabling them to strategically operationalize AI in a manner that aligns with broader organizational objectives and regulatory requirements.

Key insights

• Organizations deploying agentic AI must determine specific validation, control, and observation requirements for each use case.
• The AI-GRACE (Agentic Intelligence-Governance, Risk, Assurance, Controls, and Evidence) framework connects organizational governance directly to technical implementation of agentic AI.
• The framework aims to ensure agentic AI use cases deliver intended outcomes while simultaneously meeting applicable obligations.
• AI-GRACE uses design science and situational method engineering to facilitate contextual tailoring and reuse across different organizational contexts.
• The framework helps establish objectives and obligations for agentic AI deployments.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.21192