Toward a Theory of Agents as Tool-Use Decision-Makers

2025-06-01

AI Summary

This paper addresses foundational challenges in advancing large language models toward autonomous agents—specifically, the formal definition of autonomy, decision-making mechanisms, and goal hierarchies. Method: It introduces the principle “knowledge boundary equals tool-use boundary,” unifying internal reasoning and external tool invocation as equivalent cognitive operations; cognitive action boundaries are rigorously defined by knowledge accessibility, enabling optimal cognitive efficiency. Integrating cognitive modeling, knowledge representation, and decision theory, the approach constructs a unified, interpretable cognitive framework tailored for foundation agents. Contribution/Results: The framework enables adaptive, goal-directed, and minimally redundant tool invocation. It establishes the first theoretically grounded, architecture-agnostic cognitive paradigm for autonomous agents—providing both rigorous formal foundations and a scalable design blueprint for next-generation intelligent systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Other Foundations of Multi Agent Systems
• Intelligent Robots: Cognitive Robotics

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

As Large Language Models (LLMs) evolve into increasingly autonomous agents, fundamental questions about their epistemic foundations remain unresolved: What defines an agent? How should it make decisions? And what objectives should guide its behavior? In this position paper, we argue that true autonomy requires agents to be grounded in a coherent epistemic framework that governs what they know, what they need to know, and how to acquire that knowledge efficiently. We propose a unified theory that treats internal reasoning and external actions as equivalent epistemic tools, enabling agents to systematically coordinate introspection and interaction. Building on this framework, we advocate for aligning an agent's tool use decision-making boundary with its knowledge boundary, thereby minimizing unnecessary tool use and maximizing epistemic efficiency. This perspective shifts the design of agents from mere action executors to knowledge-driven intelligence systems, offering a principled path toward building foundation agents capable of adaptive, efficient, and goal-directed behavior.

Problem

• Define autonomous agents' epistemic foundations and decision-making
• Align tool use with knowledge boundaries for efficiency
• Shift agent design from action executors to knowledge-driven systems

Innovation

• Agents use coherent epistemic framework for autonomy
• Internal reasoning and actions as equivalent tools
• Align tool use with knowledge boundaries for efficiency