Agentic AI is significantly altering the method through which software-defined networks are managed. Consequently, judgments could be made, problems fixed on their own, and traffic patterns could be anticipated. It is actually possible for agentic AI systems to plan, consider, and execute complex actions as a result of the SDN control plane. Simple machine learning models, on the other hand, are merely capable of managing a few reaction-dependent behaviors. Even though it accelerates and improves network functioning, it poses vital questions of accountability, regulation, and safety. Lawbreakers can possibly utilize programmable network topologies to perpetrate criminal activities, grant autonomous entities powers, and trigger unforeseen problems. A robust software-defined network (SDN) policy architecture with agential AI is proposed in this paper. It is proposed that an incident response plan, governance, as well as monitoring, should be part of the architecture. The proposed framework comprises an automated incident response orchestrator, which could correct as well as rollback errors; a governance engine that checks decisions before implementing them; a real-time monitoring pipeline using telemetry; as well as a declarative policy layer that limits the actions of agents. There are various ways of improving the control loop in SDN, including making it simpler for people to understand, providing authorization based on work, and looking for compliance but at the cost of the independence of people. Tests of the prototype of the SDN. It illustrates the speed at which regulations are examined, the effectiveness of anomaly detection, and the potential for prompt resolution of hostile or unexpected agent behavior. It provides the foundation for the safe, transparent, and responsible application of agentic AI in the next generation of programmable networks.