Cross-lingual emotion detection allows us to analyze global trends, public opinion, and so- cial phenomena at scale. We participated in the Explainability of Cross-lingual Emotion De- tection (EXALT) shared task, achieving an F1- score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outper- formed the baseline by more than 0.16 F1-score absolute, and ranked second amongst compet- ing systems. We conducted experiments using fine-tuning, zero-shot learning, and few-shot learning for Large Language Model (LLM)- based models as well as embedding-based BiL- STM and KNN for non-LLM-based techniques. Additionally, we introduced two novel meth- ods: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that LLM-based approaches provided good performance on multilingual emotion de- tection. Furthermore, ensembles combining all our experimented models yielded higher F1- scores than any single approach alone.