Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability.We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively.

This paper introduces a framework that integrates fairness and transparency into advanced machine learning models, specifically LightGBM and XGBoost, applied to loan approval and house price prediction datasets. The key contribution is using fairness-focused techniques, such as Calibrated Equalized Odds and Intersectional Fairness, which are not widely studied in financial and real estate contexts. To improve model transparency, SHAP (SHapley Additive exPlanations) is utilized along with a novel fairness-based interpretability method to measure both model fairness and the importance of individual features. Through comprehensive experiments, we show that LightGBM delivers high accuracy while balancing fairness and performance effectively. The broader relevance of this work is discussed in the context of governance and regulatory requirements, highlighting the importance of responsible practices in high-stakes financial decision-making processes. This research highlights the importance of fairness and transparency in real-world applications, promoting equity, trust, and adherence to evolving legal standards, and provides practical insights for data scientists, machine learning researchers, and professionals in the real estate and financial sectors.

This paper provides a comprehensive survey on the applications, challenges, and future directions of Artificial Intelligence (AI) in the insurance and real estate sectors. We explore key AI-driven solutions, such as advanced risk assessment, predictive analytics for fraud detection, and smart building management, highlighting their impact on enhancing operational efficiency and decision-making processes. The survey covers a wide range of AI techniques, including machine learning, deep learning, and natural language processing, and discusses their specific applications within these industries. We address critical challenges, such as data quality issues, the need for model interpretability, regulatory compliance, and integration with existing systems. Additionally, we identify emerging trends, such as the adoption of reinforcement learning for dynamic pricing and the use of AI for personalized insurance products. The paper concludes by outlining significant research gaps and proposing a roadmap for future work, aimed at guiding the development of more robust, explainable, and ethically sound AI applications in insurance and real estate.