Coordinating a massive population of flexible grid‑edge devices can reduce the need for time‑consuming and capital‑intensive network upgrades, and AI‑based control methods such as multi‑agent reinforcement learning or imitation learning offer real‑time decision scalability. Nevertheless, system operators still require an independent and rigorous way to decide whether a particular AI system is safe enough for deployment. This paper introduces a finite‑sample probabilistic safety certification framework for black‑box AI decision models operating in closed‑loop grid environments. The central idea reduces the full input‑AI‑grid evaluation pipeline to a binary unsafe outcome under an operator‑defined safety specification, and then applies exact binomial inference to certify the probability of unsafe operation. Given a set of held‑out calibration scenarios, the framework returns the tightest one‑sided upper certificate together with an accept/reject deployment criterion that controls the probability of false safety certification. Because the certification pertains to the calibration distribution, which may differ from future operation, we further combine the nominal certificate with physically interpretable sample‑space adversarial attacks—a concept widely used in AI to probe model fragility. Case studies on grid‑edge flexibility coordination with a 1,000‑agent AI model (independent parameters) confirm the finite‑sample safety guarantee and illustrate the benefit of integrating adversarial attacks into a rolling‑window training‑certification‑deployment workflow.
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