Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
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 framework that treats agents as making sequential decisions about 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 criterion for tool use that complements existing decision-theoretic models and is essential for building agents that are not only correct, but increasingly intelligent.
Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 25 of its 47 references that are in the map. First posted on arXiv 1 Jun 2025.
Builds on
25 of 47 references.
Closest ancestors · 1
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Other references · 24
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- 2024LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robotscited by 18
- 2023SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Designcited by 13
- 2025Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalystcited by 6
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Citing papers per year: 2025: 12; 2026: 10.
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- 2026Beyond the Black Box: Interpretability of Agentic AI Tool Use7 May 2026
- 2026TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering7 May 2026
- 2026MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security8 Apr 2026
- 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents2 Feb 2026
- 2025From Word to World: Can Large Language Models be Implicit Text-based World Models?21 Dec 2025
- 2025When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents2 Dec 2025
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- 2025Agentic Entropy-Balanced Policy Optimization16 Oct 2025
- 2025Code2Video: A Code-centric Paradigm for Educational Video Generation1 Oct 2025
- 2025ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models26 Sep 2025
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- 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Survey2 Sep 2025
- 2025Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 20251 Sep 2025
- 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence28 Jul 2025
- 2025A Survey of Context Engineering for Large Language Models17 Jul 2025
- 2025A Protocol for Trustless Verification Under Uncertainty1 Jul 2025
- 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
- 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170
- 2025Agentic Entropy-Balanced Policy Optimizationcited by 40
- 2025UserRL: Training Interactive User-Centric Agent via Reinforcement Learningcited by 32
- 2026Self-Improvements in Modern Agentic Systems: A Surveycited by 19
- 2025When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agentscited by 15
- 2026Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Usecited by 7
- 2026When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer Morecited by 6
- 2026Beyond the Black Box: Interpretability of Agentic AI Tool Usecited by 3
- 2026MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Securitycited by 2
- 2026CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perceptioncited by 1
- 2026Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commercecited by 0
12 most cited of 13 All 13 (JSON)
- 2026When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMs28 Sep 2026
- 2026CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perception9 Aug 2026
- 2026Self-Improvements in Modern Agentic Systems: A Survey14 Jul 2026
- 2026Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce23 Jun 2026
- 2026When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More12 Jun 2026
- 2026Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use13 May 2026
- 2026Beyond the Black Box: Interpretability of Agentic AI Tool Use7 May 2026
- 2026MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security8 Apr 2026
- 2025When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agents2 Dec 2025
- 2025Agentic Entropy-Balanced Policy Optimization16 Oct 2025
- 2025UserRL: Training Interactive User-Centric Agent via Reinforcement Learning24 Sep 2025
- 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Survey2 Sep 2025
12 newest of 13 All 13 (JSON)
- 2026TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steeringcited by 6
- 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agentscited by 5
- 2025LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledgecited by 3
- 2026TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering7 May 2026
- 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents2 Feb 2026
- 2025LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledge3 Nov 2025
- 2025A Survey of Context Engineering for Large Language Modelscited by 126
- 2025Code2Video: A Code-centric Paradigm for Educational Video Generationcited by 27
- 2025Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025cited by 14
- 2025ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Modelscited by 10
- 2025A Protocol for Trustless Verification Under Uncertaintycited by 2
- 2025Code2Video: A Code-centric Paradigm for Educational Video Generation1 Oct 2025
- 2025ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models26 Sep 2025
- 2025Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 20251 Sep 2025
- 2025A Survey of Context Engineering for Large Language Models17 Jul 2025
- 2025A Protocol for Trustless Verification Under Uncertainty1 Jul 2025
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