The talk introduces an agentic AI framework centered on how role-specialized LLM agents can support decision-making, adaptation under non-stationary environment, and system validation and verification (V&V) for trustworthy autonomous system. Our framework provides two complementary directions. First, ATLAS proposes agentic self-evolution, where an evolving agent within the core algorithm EvoDPO is improved over a long-horizon by task-distributed supporter LLM agents responsible for exploration, fine-tuning strategy, and adaptive reference-policy gating mechanism. This project targets progressive agent development under drifting environment and demonstrates domain adaptation for sustained improvement in changing domains, such as non-stationary bandits and scientific machine learning. Second, AIVV conducts agentic verification and validation, where mathematically grounded anomaly detection is strictly validated by role-specialized LLM agents that verify failure modes of the system again natural-language requirements. This framework distinguishes nuisance faults from true faults and produces actionable engineering protocols, such as gain-tuning parameter proposals. Together, these projects define a common paradigm: Agentic AI as a structured orchestration that combines statistical or optimization-based inner loops with role-based LLM oversight to achieve scalable adaptation, reliability, and safety. This perspective moves beyond viewing LLM agents as standalone assistants, instead positioning them as coordinating components for progressive self-improving, and trustworthy system V&V intelligent systems.