E-commerce ad platforms enforce content policies and review created ads before publication, with casing requirements playing a critical role in maintaining readability and brand consistency. Existing NER-based transformer models have been widely used for casing correction, but they process characters independently in a classification-based manner, failing to capture sentence level contextual dependencies, making them less reliable when handling unseen or ad-specific terms, e.g., brand names. LLMs like ChatGPT offer better generalization to proper nouns, but they are expensive and have high latency. Besides, generative model can suffer from hallucination. To address these challenges, we propose a two-stage approach: (1) an LLM-based Agent leveraging Chain-of-Actions (CoA) to enforce casing policies while accurately handling ads-specific terms, such as brand names, and (2) a lightweight generative model that preserves the LLM Agent’s knowledge while significantly reducing latency and costs. We design a novel in-context decoding strategy, which avoids hallucinations. Our approach outperforms NER-based methods and achieves near-LLM Agent performance, making it a scalable and efficient solution for real-world ad compliance automation.