Microfinance institutions (MFIs) play a crucial role in promoting financial inclusion in developing economies such as Nepal. However, the increasing rate of non-performing loans (NPLs) threatens the sustainability of the microfinance sector. Traditional credit monitoring methods are often reactive and lack predictive capabilities for early detection of loan defaults. MThis study proposes an Agentic AI-based Early Warning System (EWS) for predicting non-performing loans in Nepalese microfinance institutions. The proposed framework integrates machine learning algorithms, autonomous AI agents, and explainable AI mechanisms to analyze borrower data and generate real-time risk alerts The system utilizes financial transaction data, borrower demographic profiles, repayment histories, and behavioral indicators to predict loan default probability. Experimental evaluation using ensemble machine learning models demonstrates improved predictive accuracy compared to traditional credit scoring approaches. The proposed framework contributes to FinTech innovation by enabling proactive credit risk management, improving loan portfolio quality, and supporting regulatory oversight within Nepal's microfinance ecosystem.