Measuring which cultural values a languagemodel agent falls back on when it must act is a fundamental problem in multilingual and culturally-grounded evaluation. Current agent benchmarks are designed primarily to measure task success in high-resource languages such as English, which creates major limitations when the question is whose culture an agent’s committed action encodes. The key challenges are the absence of an acting-based value-conflict benchmark and the difficulty of preserving culturallyloaded meaning when a scenario is carried into a low-resource language such as Bangla. In this paper, to study these cultural defaults under action, we present CultAct-ML, a benchmark of 39 value-conflict dilemmas recast as secondperson agent actions and evaluated with a twophase plan–act agent over English, Indonesian, and Bangla. We further introduce three metrics that measure the Western-cluster default, its reliability across languages, and the drift of the agent’s plan, and every Bangla item is verified by a native speaker. Across three models and three repeats, a total of 1,053 plan–act decisions are produced, all valid. We find that a Western default holds above chance for Qwen2.5-72B and Kimi K2 in every language but fades to chance for Qwen3-235B outside English. After stratifying the plan drift by the language of the plan, we further find that Qwen3-235B plans in English rather than Bangla on 70.1% of Bangla scenarios, highlighting a cross-language effect that a pooled analysis hides entirely. The scenarios, code, and all metrics are publicly released.1