Can AI Scientists Coordinate at Runtime?

Multi-agent AI Scientists have shown improving performance across a diverse range of different tasks. Yet a common approach is design-time agentic orchestration, which typically relies on fixed workflows. In contrast, human scientists coordinate and adjust their division of labor at runtime. We therefore ask: can AI Scientists also coordinate at runtime? To this end, we introduce Runtime Agent Coordination (\rac), which selects agents from existing AI Scientist hosts during execution, assigns scoped work contracts, and provides artifact-grounded verification. Verification informs subsequent agents without blocking transitions or discarding artifacts. We evaluate one shared implementation across ARK, Agent Laboratory, and EvoScientist on ResearchClawBench under matched models, tools, permissions, and run-level budgets. Four cumulative conditions separate native execution, runtime communication, runtime selection, and the combined addition of contracts and verification. Our results highlight the potential of runtime coordination to improve research quality, while revealing host-dependent trade-offs between coordination overhead and performance under matched budgets.