Critique of Agent Model
Eric Xing, Mingkai Deng, Jinyu Hou
Why It Matters
What makes this one worth your time
Understanding and defining agency in AI is crucial for developing systems that can operate autonomously and safely in real-world environments.
The paper proposes a new architecture to define and enhance agency in AI systems.
Summary
The paper critiques the concept of agency in AI systems, distinguishing between agentic systems with externally engineered workflows and agentive systems with internalized capabilities. It proposes a Goal-Identity-Configurator (GIC) architecture for general-purpose agents, emphasizing autonomy and self-directed learning.
Key contributions
- Proposes the Goal-Identity-Configurator (GIC) architecture for AI agents.
- Analyzes agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning.
Notable insights
- The distinction between agentic and agentive systems highlights the importance of internalized capabilities for true autonomy.
- The GIC architecture combines hierarchical goal decomposition and identity evolution with simulative reasoning.
Possible limitations
- Not stated in the abstract
Abstract
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.