When organizations deploy agentic artificial intelligence, they must go beyond judging model trustworthiness and specify what to validate, control, and observe for a use case to achieve its intended outcome while complying with obligations. The AI‑GRACE (Agentic Intelligence‑Governance, Risk, Assurance, Controls, and Evidence) framework links organizational governance with technical implementation, offering a systematic approach to operationalize use cases.\ \ The framework starts by defining objectives and obligations, then assesses risks across seven domains, including mission fulfillment and value realization. The risk assessment yields pre‑deployment assurance requirements, runtime control measures, and evidence collection needs, which guide capability qualification, gap analysis, and logical architecture design.\ \ Technically, AI‑GRACE introduces an Agent Operating Envelope that delineates permitted actions and escalation triggers, and Risk‑Aligned Independence Levels (RAIL) that summarize the authorized degree of autonomy.\ \ A fictional retail banking scenario illustrates the method in practice, showing how an organization can trace a path from high‑level goals to concrete controls and evidence, while identifying existing capabilities and unresolved risks.\ \ The main contribution is a traceable decision basis that helps organizations determine what must be implemented, what is already supported, and what remains open. Empirical evaluation is required to confirm its impact on deployment decisions, efficiency, and reuse.\ \ Review