This paper describes the submission by **Team Bhramastra** for the **#SMM4H-HeaRD 2026** Shared Task 1, focused on personal Adverse Drug Event (ADE) detection in multilingual social media. A decoupled architecture, **Hunter-Judge**, is proposed to handle extreme class imbalance and linguistic variance across seven languages, including a surprise zero-shot Farsi set. The system employs a fine-tuned multilingual mDeBERTa-v3 model as a high-recall filter (**Hunter**), followed by a Gemini-2.5-Flash model (**Judge**) optimized via the **DSPy** framework for precision-oriented agentic adjudication. By implementing a reasoning protocol grounded in clinical RAG evidence and universal ingredient mapping, the pipeline achieved the **highest average F1-score (0.6653)** among all teams. Strong zero-shot generalizability on Farsi (**F1: 0.5863**) was demonstrated, highlighting the effectiveness of medically-grounded adjudication in low-resource contexts.