Sensor networks are distributed real-time embedded (DRE) systems that often operate in open environments where operating conditions, workload, resource availability, and connectivity cannot be accurately characterized a priori. As with other open DRE systems, they must perform sequences of heterogeneous data collection, manipulation, and coordination tasks to meet specified system objectives. The South East Alaska MOnitoring Network for Science, Telecommunications, Education, and Research (SEAMONSTER) project illustrates many common system management and dynamic operation challenges in a representative sensor network, including adapting to changes in network topology, effective reaction to local environmental changes, and power management through system sleep/wake cycles. This paper discusses a case study for applying middleware and autonomous agent technologies from the Multi-agent Architecture for Coordinated Responsive Observations (MACRO) to these challenges in the SEAMONSTER sensor network.