Abstract This article presents an overview of the SmartSite research project that adopts machine learning, decision theory and distributed artificial intelligence to design and test a multi-agent system (MAS) for asphalt road construction. SmartSite puts major emphasis on sensing and communication technologies that integrate real-time automated information exchange in the supply chain of road construction. As part of the larger SmartSite project, this article introduces a novel real-time path planning system for compactors and presents the results of several simulation and field realistic experiments conducted to evaluate the system in a sophisticated simulation and harsh construction environment, respectively. The system operates based on Belief-Desire-Intention (BDI) software agents and real-time sensory inputs. The newly developed integrated and information rich process benefits asphalt compactor operators, as they are now capable to control their machinery and react to changing environmental, material-related and process-related disturbances or changes. This improves the quality of the delivery and laying of asphalt material, prevents compactors from over-compacting certain road segments, increases the road's pavement longevity during the operational life cycle phase; refocuses the work tasks of the site managers, and reduces the construction budget and schedule. The system's ability to maneuver an asphalt roller during real-word operation also makes it an important step towards a fully automated asphalt compactor.