Toward a Theory of Agents as Tool-Use Decision-Makers
This foundational work addresses the epistemic underpinnings of autonomous agents, offering a principled framework for building adaptive and efficient AI systems, though it is incremental as it builds on existing concepts.
The paper tackles the problem of defining and designing autonomous agents by proposing a unified theory that treats internal reasoning and external actions as epistemic tools, aiming to align tool use with knowledge boundaries for efficient behavior.
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.