Translating a story renders its words into a new language; adapting it transposes its world into a new culture. Large Language Models (LLMs) translate and adapt individual passages effectively, but long-form novels spanning ∼100 chapters suffer model failures. We formalize long-form cross-cultural story adaptation as a generation task parameterized by source and target language-culture pairs, with three structural axes: A1 identity coherence across narrative arc, A2 cultural-system alignment within scenes, and A3 dependency-chain integrity among related entities. This is addressed through a five-stage agentic adaptation framework comprising entity registry, target-culture plan, dependency-aware batched localization, validation and rewrite. Evaluation is conducted through ablations across three language-culture pairs and two model families; the proposed system outperforms across all three axes, reducing identity errors by 65%, dependency errors by 64%, and cultural misalignment errors by 27% against competitive baselines. Further validation through a production A/B test on a commercial storytelling platform demonstrates listener retention comparable to that for human-adapted content, reflecting viability of the framework.