Framework guides organizations to deploy trustworthy agentic AI safely

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

Artificial IntelligenceComputers and SocietyMultiagent Systems

Summary

Organizations using AI that can act on its own need more than just trusting the AI model. They also have to figure out what to check, control, and watch to make sure the AI does what it is supposed to and follows rules. The authors propose a framework called AI-GRACE that helps connect an organization’s goals and rules with how the AI is actually built and run. This framework helps decide what requirements the AI system must meet before it is launched, what controls it should have during use, and what evidence should be gathered. They also describe ideas like setting limits on the AI’s actions and defining how independent the AI can be.

What this means in practice

  • For ai governance teams: Create clear connections between organizational ethics and specific AI deployment controls for trustworthy agentic AI systems.
  • For enterprise ai architects: Design AI systems with traceable requirements and control mechanisms aligned to organizational risks and obligations for agentic AI use cases.

A position paper. It proposes an approach and reports no results.

Authors

John Cuneo, David Chun, Gaurav Khanna

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.
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