The Cartesian Cut in Agentic AI

2026-04-08

AI Summary

This work addresses the inherent tension among autonomy, robustness, and governability in large language model (LLM)-driven agents. To reconcile these competing demands, the authors propose the “Cartesian Agent” architecture, which explicitly decouples control states from policy through a symbolic interface, thereby coupling the predictive capabilities of LLMs with an engineered runtime system. The framework incorporates hierarchical feedback loops and runtime intervention mechanisms, and formally distinguishes three control paradigms: bounded services, Cartesian agents, and integrated agents, highlighting how the locus of control critically influences system performance. Experimental results demonstrate that the Cartesian architecture offers significant advantages in modularity, governability, and startup efficiency, while also revealing limitations related to sensitivity and potential bottlenecks.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

LLMs gain competence by predicting words in human text, which often reflects how people perform tasks. Consequently, coupling an LLM to an engineered runtime turns prediction into control: outputs trigger interventions that enact goal-oriented behavior. We argue that a central design lever is where control resides in these systems. Brains embed prediction within layered feedback controllers calibrated by the consequences of action. By contrast, LLM agents implement Cartesian agency: a learned core coupled to an engineered runtime via a symbolic interface that externalizes control state and policies. The split enables bootstrapping, modularity, and governance, but can induce sensitivity and bottlenecks. We outline bounded services, Cartesian agents, and integrated agents as contrasting approaches to control that trade off autonomy, robustness, and oversight.

Problem

• Cartesian agency
• control architecture
• LLM agents
• symbolic interface
• autonomy-robustness tradeoff

Innovation

• Cartesian agency
• symbolic interface
• control architecture
• modularity
• LLM agents