Agentic Artificial Intelligence systems, characterized by autonomous reasoning, memory augmentation, and adaptive planning, are rapidly reshaping technological landscapes. Unlike traditional AI or large language models, agentic AI integrates decision-making with persistent execution, enabling complex interactions across dynamic environments. However, this evolution introduces novel security risks, governance challenges, and ethical considerations that current frameworks inadequately address. This survey provides a cross-layer review of agentic AI, encompassing architectural paradigms, threat taxonomies, and governance strategies. It consolidates findings from adjacent domains such as cybersecurity, AI safety, multi-agent coordination, and ethics, offering a holistic understanding of vulnerabilities and mitigation approaches. We integrate insights from recent advances in defense architectures and governance innovations, highlighting the limitations of static policies in addressing dynamically evolving threats. Real-world deployments from industrial automation to military and policy applications reveal both successful integrations and notable failures, underscoring the urgency of resilient oversight mechanisms. Furthermore, we identify critical research gaps in benchmarking, memory integrity, adversarial defense, and normative embedding, emphasizing the need for interdisciplinary collaboration to develop adaptive, accountable, and transparent systems. This review serves as a narrative synthesis rather than a systematic literature review, aiming to bridge technical, governance, and ethical perspectives. By integrating cross-disciplinary findings, it lays the foundation for future research on securing, aligning, and governing agentic AI in real-world contexts. Ultimately, this work calls for cooperative innovation to ensure that agentic AI evolves as a trustworthy, accountable, and beneficial technology.
ADABARA et al. (2025) studied this question.