Agentic AI systems autonomously make decisions to achieve goals with minimal human intervention, characterized by autonomy, goal-directed behavior, and adaptability. Unlike traditional systems, these AI systems can dynamically adjust their actions and learn from ongoing processes, making them essential in fields such as customer support and cybersecurity. Evaluating these systems is complex due to factors like branching inputs, multi-step decision processes, and component interdependencies, requiring continuous adaptation and consideration of safety and alignment. Raga AI's Holistic 8-Step Framework addresses these challenges by offering structured methodologies, while Deepchecks provides tools for real-time monitoring and evaluation, facilitating comprehensive assessments through synthetic trajectories, end-to-end component checks, and emergent-behavior detection. Real-world applications include healthcare and financial services, where Agentic AI systems improve processes by ensuring guideline adherence and enhancing model risk management.