Researchers introduce Runtime Agent Coordination (RAC), a system that enables AI scientists to coordinate and adjust their division of labor at runtime. This approach selects agents from existing AI-scientist hosts, assigns work contracts, and provides artifact-grounded verification. An exploratory evaluation shows that runtime coordination yields better results than native execution, but the addition of contracts and verification can have mixed outcomes.