Critique of Agent Model

Researchers critique current AI agent models, arguing that genuine agency requires internalized goal, identity, decision-making, self-regulation, and learning structures rather than external scaffolding. They propose a Goal-Identity-Configurator (GIC) architecture for general-purpose agents and discuss safety and controllability of autonomous systems.

arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model LLM systems marketed as coding agents'', AI co-scientists'', and other agentic" tools that promise to drive up productivity, and at the same time, existential" concerns such as AI escaping human control with destructive power under a speculative machine agency" against humans, it has become essential to clarify where automation ends and agency begins, both for building capable systems and for understanding whether and what to fear. Drawing on Descartes' grounding of agency in independent thought, and on portrayals of autonomous beings in science fiction, we survey the current landscape of AI agents, and analyze agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning. Specifically, we argue that genuine agency requires these structures to be \emph{internalized within the system itself} rather than assembled through external scaffolding. This distinction between \emph{agentic} systems, whose competence resides in engineered workflows, and \emph{agentive} systems, whose capabilities including social interaction arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy. Building on this analysis, we propose the Goal-Identity-Configurator GIC architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Furthermore, we share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and agency", but remain under human oversight.