The rapid proliferation of generative artificial intelligence (AI) has introduced a new class of cyber threats, including AI-powered phishing, deepfakes, adversarial machine learning, and autonomous botnets. Unlike traditional human-crafted attacks, these AI-driven threats are scalable, adaptive, and capable of evading conventional detection mechanisms. Existing defenses, such as spam filters, forensic detectors, and rule-based intrusion prevention, exhibit poor generalization, with benchmarks showing up to 50% performance degradation on in-the-wild datasets. To address this paradigm shift, this study investigates the role of Agentic AI autonomous systems with reasoning, planning, and tool orchestration capabilities in countering AI-enabled attacks. We highlight the importance of prioritized and adaptive datasets (with text-based phishing and audio deepfake detection as immediate needs) and propose tiered validation frameworks—sandbox testing, pilot deployments, and continuous red team certification—to ensure reliability before deployment in mission-critical Security Operations Centers (SOCs).Drawing on advances in stylometric detection, LLM-based phishing classifiers, audio-visual deepfake benchmarks, reinforcement learning (RL)-driven cyber defense, and deception frameworks, we evaluate how Agentic AI can enhance detection precision, enable adaptive mitigation, and maintain adversarial resilience. We further emphasize Human– AI teaming as central to explainability, trust, and accountability, and outline governance mechanisms that embed proportionality, transparency, and civil liberty protections into defensive deployments. Our findings suggest that Agentic AI, when deployed with technical rigor and ethical safeguards, can reduce the mean time to detection and response while preserving trust and civil liberties, marking a shift from static defenses to proactive, adaptive, and responsible cyber resilience.