CISOs should not reject agentic AI outright, but instead use four control questions (data inputs, actions, damage scope, observability) to make risks legible and deliberately constrain them.
SEED leverages self-generated hindsight supervision from language model-native trajectory analysis to bridge the supervision gap between episode-level outcomes and token-level learning signals.
Agentic AI systems create security risks through their autonomy, which classical threat models do not cover and which require different control mechanisms.
AI agents are not a universal automation solution – for structured processes, rule-based systems and RPA are more robust, cost-effective, and easier to control.
As AI technology matures, enterprise-wide scaling is increasingly hindered by organizational gaps, insufficient management expertise, and low employee adoption—not technical limitations.
Agentic AI shifts the boundary between human and machine from individual tasks to responsibility and control, but requires new governance structures and open architectures to ensure EU AI Act compliance and investment security.
