Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary
from Best AI papers explained Hosted by Enoch H. Kang
This position paper discusess Theory of Agent (ToA), a framework that redefines large language model agents as decision-makers who must choose between internal reasoning and external tool use. The authors argue that agents should only invoke external tools when epistemically necessary, meaning the task cannot be reliably solved using the model's existing internal knowledge and logic. This perspective addresses common failures like overthinking and overacting, which occur when an agent's internal solvability estimates are poorly calibrated. By treating reasoning and acting as co-equal methods for reducing uncertainty, the framework highlights that unnecessary delegation to tools can stagnate the growth of an agent's internal intelligence. Ultimately, the research suggests that alignment should be measured by how effectively an agent allocates epistemic effort rather than just achieving a correct answer. These principles offer a new trajectory for training and evaluating agents to ensure they become more autonomous and efficient over time.
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