## Agentic AI for Credit Risk Assessment The increasing digitalization of financial services has created a strong demand for autonomous, transparent, and real-time decision-making systems for credit risk assessment. Traditional machine learning models, while effective in pattern recognition, often lack the adaptability, situational awareness, and autonomy needed in modern financial operations. This study proposes an Agentic AI framework, in which AI agents autonomously assess credit risk, making decisions based on clear and interpretable decision-making paths. The system includes a multi-agent system with reinforcing learning, natural language reasoning, explainable AI modules, and real-time data absorption pipelines. ## Advantages and Limitations Findings indicate that the proposed system offers greater decision speed, transparency, and responsiveness compared to traditional credit scoring models. However, some practical limitations remain, such as the risks of model drift, inconsistencies in interpreting high-dimensional data, regulatory uncertainties, and infrastructure limitations in low-resource settings. ## Future Perspectives The system has a high potential to transform credit analytics. Future research should focus on dynamic regulatory compliance mobilizers, new agent teamwork, adversarial robustness, and large-scale implementation in cross-country credit ecosystems. In general, Artificial Intelligence is transforming the financial sector, automating processes, improving efficiency, and opening new opportunities. However, the adoption of these technologies requires careful consideration of the risks and ethical implications.