Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary

Hongru Wang, Cheng Qian, Manling Li, Jiahao Qiu, Boyang Xue, Mengdi Wang, Heng Ji, Amos Storkey, Kam-Fai Wong
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:172470-172485, 2026.

Abstract

As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically treat tools as ordinary actions and optimize for task success or reward, offering little principled distinction between epistemically necessary interaction and unnecessary delegation. This position paper argues that agents should invoke external tools only when epistemically necessary. Here, epistemic necessity means that a task cannot be completed reliably via the agent’s internal reasoning over its current context, without any external interaction. We introduce the Theory of Agent (ToA), a unified framework that reconceives agents as tool-use decision makers under epistemic uncertainty to decide whether remaining uncertainty should be resolved internally or delegated externally. From this perspective, common agent failure modes (e.g., overthinking and overacting) arise from miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. We further discuss implications for training, evaluation, and agent design, highlighting that unnecessary delegation not only causes inefficiency but can impede the development of internal reasoning capability. Our position provides a normative and fundamental criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.

Cite this Paper


BibTeX
@InProceedings{pmlr-v306-wang26mb, title = {Position: Agents Should Invoke External Tools {ONLY} When Epistemically Necessary}, author = {Wang, Hongru and Qian, Cheng and Li, Manling and Qiu, Jiahao and Xue, Boyang and Wang, Mengdi and Ji, Heng and Storkey, Amos and Wong, Kam-Fai}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {172470--172485}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/wang26mb/wang26mb.pdf}, url = {https://proceedings.mlr.press/v306/wang26mb.html}, abstract = {As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically treat tools as ordinary actions and optimize for task success or reward, offering little principled distinction between epistemically necessary interaction and unnecessary delegation. This position paper argues that agents should invoke external tools only when epistemically necessary. Here, epistemic necessity means that a task cannot be completed reliably via the agent’s internal reasoning over its current context, without any external interaction. We introduce the Theory of Agent (ToA), a unified framework that reconceives agents as tool-use decision makers under epistemic uncertainty to decide whether remaining uncertainty should be resolved internally or delegated externally. From this perspective, common agent failure modes (e.g., overthinking and overacting) arise from miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. We further discuss implications for training, evaluation, and agent design, highlighting that unnecessary delegation not only causes inefficiency but can impede the development of internal reasoning capability. Our position provides a normative and fundamental criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.} }
Endnote
%0 Conference Paper %T Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary %A Hongru Wang %A Cheng Qian %A Manling Li %A Jiahao Qiu %A Boyang Xue %A Mengdi Wang %A Heng Ji %A Amos Storkey %A Kam-Fai Wong %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-wang26mb %I PMLR %P 172470--172485 %U https://proceedings.mlr.press/v306/wang26mb.html %V 306 %X As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically treat tools as ordinary actions and optimize for task success or reward, offering little principled distinction between epistemically necessary interaction and unnecessary delegation. This position paper argues that agents should invoke external tools only when epistemically necessary. Here, epistemic necessity means that a task cannot be completed reliably via the agent’s internal reasoning over its current context, without any external interaction. We introduce the Theory of Agent (ToA), a unified framework that reconceives agents as tool-use decision makers under epistemic uncertainty to decide whether remaining uncertainty should be resolved internally or delegated externally. From this perspective, common agent failure modes (e.g., overthinking and overacting) arise from miscalibrated decisions under uncertainty rather than deficiencies in reasoning or tool execution alone. We further discuss implications for training, evaluation, and agent design, highlighting that unnecessary delegation not only causes inefficiency but can impede the development of internal reasoning capability. Our position provides a normative and fundamental criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.
APA
Wang, H., Qian, C., Li, M., Qiu, J., Xue, B., Wang, M., Ji, H., Storkey, A. & Wong, K.. (2026). Position: Agents Should Invoke External Tools ONLY When Epistemically Necessary. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:172470-172485 Available from https://proceedings.mlr.press/v306/wang26mb.html.

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