AI-agents are steadily changing how road services are delivered. Inspired by platforms that analyse video, sensor and GIS data to spot road-defects like cracks, potholes and faded markings, these systems make the shift from “fix it when it breaks” to “monitor it before it breaks”. For example, by detecting anomalies on the pavement early and tagging their exact location, road-asset teams can prioritise repairs more intelligently. Beyond defect detection, AI-agents help build automatic inventories of road assets (signs, lane-markings, barriers) and map their condition across large networks. They also forecast which stretches of road will degrade next based on usage, weather and underlying materials-enabling preventive maintenance and smarter budgeting. Looking ahead, the future scope is significant: these agents will blend with autonomous inspection vehicles, drones, and real-time traffic feeds to deliver continuous monitoring at scale. They’ll tighten the loop between data and action-repair crews dispatched instantly, budgets allocated dynamically, and entire networks optimised for safety and cost. Ultimately, as these agents mature, road services will become more predictive, resilient and cost-efficient than ever.