Public agencies are testing “agentic” AI, software that watches incoming work, remembers prior cases, plans multi-step tasks, and completes routine actions with limited supervision. These systems promise faster services and less administrative burden. They also shift discretion, change lines of accountability, and raise new questions about equity and trust. This paper examines how agentic AI is likely to reshape street-level work across permits, benefits, and inspections. It explains what makes agentic tools different in practice, shows how roles and routines change at the frontline, and outlines safeguards that align with public law and values. Drawing on research about algorithmic decision making and human–automation interaction, and on recent guidance in the United States and Canada, the paper argues that governments can gain speed without losing legitimacy if they require plain-language reasons, keep people in the loop when impact is high or the system is uncertain, audit accuracy and fairness, log decisions, and pilot before scaling. It closes with simple service measures, processing time, errors, appeals, overrides, escalation, and disparity gaps, to track progress and protect rights.