Critique of the Agent Model: Distinguishing Automation from Genuine Agency in AI Systems

What is an agent? What constitutes agency?

Genuine agency requires these structures to be internalized within the system itself rather than assembled through external scaffolding.

This distinction between agentic systems, whose competence resides in engineered workflows, and agentive systems, whose capabilities arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy.

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.

We share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and 'agency', but remain under human oversight.