Critique of the Agent Model
The word agent now covers everything from a for-loop with tool calls to speculative machine superintelligence, which makes it nearly useless as a technical term. This position paper from Eric Xing and collaborators tries to fix that by asking what an agent actually is and what agency consists of, drawing on Descartes and on science-fiction portrayals of autonomous beings to ground the discussion.
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Five dimensions of agency: The authors analyze agent architectures along goal, identity, decision-making, self-regulation, and learning, and argue that genuine agency requires these structures to be internalized in the system rather than assembled through external scaffolding.
Agentic versus agentive: They draw a sharp line between agentic systems, whose competence lives in engineered workflows, and agentive systems, whose capabilities including social interaction arise endogenously, marking the boundary between task-specific tools and open-world autonomy.
A concrete architecture: Building on the analysis, they propose the Goal-Identity-Configurator, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from real and simulated experience.
Why it matters: Clear definitions are not academic hair-splitting here. They shape what we build and what we should reasonably fear, and the paper centers auditability, controllability, and safety for systems that hold more autonomy yet stay under human oversight.