Agentic AI systems encounter several critical challenges. First, goal alignment: translating high-level objectives into actionable sub-tasks can cause misinterpretation. Second, real-time learning: adapting to new data in dynamic environments risks instability and model drift. Third, error recovery: detecting and correcting failures without human intervention is complex.