Coordination‐first or coordination‐last? AI agents in joint activity with human roles
TL;DR
This paper contrasts Coordination‐First architectures that explicitly address the competencies and constraints of coordinated joint activity across multiple roles and layers with the current Coordination‐Last paradigm that focuses primarily on deploy‐fast‐and‐fix‐later strategies.
Abstract
Agentic AI is the latest round of advances intended to increase what machines can do by themselves. As new technological possibilities enable a wider range of valued and critical services for human stakeholders, the usual assumption is that merely deploying more capable software/AI agents will prove sufficient for carrying out a broader range of real‐world activities. However, the real world inevitably generates surprises—in the form of anomalies, unanticipated opportunities, or as system evolution expands relationships, pressures, and interdependencies—that challenge the system and create demands beyond what any agent or set of agents can handle on their own. Thus, coordinated joint activity becomes another important layer of system function, and when agents of any type lack this competency, system performance will have surpising limits. Unfortunately, software/AI agents have not yet been designed to carry out or support the foundations for coordinated activity. This paper contrasts Coordination‐First architectures that explicitly address the competencies and constraints of coordinated joint activity across multiple roles and layers with the current Coordination‐Last paradigm that focuses primarily on deploy‐fast‐and‐fix‐later strategies. We situate this debate in the context of recent results in theories of joint activity design and organized complexity. The result is new directions for architecting coordinated activity across human and software/AI agent roles in layered networked systems.