We present Team INSALyon2’s agentic approach to SemEval-2025 Task 10 Subtask 2, which focuses on the multi-label classification of narratives in news articles across five languages. Our system employs a zero-shot architecture where specialized Large Language Model (LLM) agents handle binary classification tasks for individual narrative/subnarrative labels, with a meta-agent aggregating these decisions into final multi-label predictions. Instead of fine-tuning on the dataset, we leverage AutoGen to orchestrate multiple GPT-based agents, each responsible for detecting specific narrative/subnarrative types in a modular framework. This agent-based approach naturally handles the challenge of multi-label classification by enabling parallel decisions across the two-level taxonomy. Experiments on the English subset demonstrate strong performance with our system achieving F1_macro_coarse = 0.513, F1_sample = 0.406, securing third place in the competition. Our findings show that zero-shot agentic approaches can be competitive in complex classification tasks.