A burgeoning literature scrutinizes the interactions between humans and algorithms and how agency is (re)distributed between the two parties in the interaction. However, much of this work theorizes two-sided interactions, overlooking multi-agent settings where numerous humans and multiple algorithms may be involved. To understand the redistribution of agency in human-algorithm interaction in multi-agent settings, we conducted a qualitative case study of an open-source artificial pancreas system, in which individuals with Type 1 diabetes interact with other human and nonhuman actors to manage their medical condition. Using a relational agency lens, we unearth the detailed practices in multi-agent human-algorithm interaction to create an initial typology of practices that depict the complexity of collective agency dynamics in multi-agent human-algorithm interaction.