mlsanityData to 14 Oct 2026

Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary

arXiv 2506.00886 ↗First posted 1 Jun 2025cs.AIcited by 22

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

Same direction, newest first

  1. 2023ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings19 May

Other references · 24

  1. 2020Scaling Laws for Neural Language Modelscited by 6,586
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  4. 2024OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environmentscited by 1,195
  5. 2024AFlow: Automating Agentic Workflow Generationcited by 432
  6. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
  7. 2023ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solvingcited by 282
  8. 2025Training Language Models to Reason Efficientlycited by 247
  9. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  10. 2023AutoAgents: A Framework for Automatic Agent Generationcited by 216
  11. 2025The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Taskscited by 152
  12. 2024Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Makingcited by 144
12 more
  1. 2025Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolutioncited by 116
  2. 2024Self-Discover: Large Language Models Self-Compose Reasoning Structurescited by 110
  3. 2024SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationalescited by 105
  4. 2025Acting Less is Reasoning More! Teaching Model to Act Efficientlycited by 80
  5. 2025OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoningcited by 69
  6. 2023Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMscited by 62
  7. 2025AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgetingcited by 50
  8. 2024Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questionscited by 30
  9. 2024AppBench: Planning of Multiple APIs from Various APPs for Complex User Instructioncited by 24
  10. 2024LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robotscited by 18
  11. 2023SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Designcited by 13
  12. 2025Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalystcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 10.

22 papers.

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By area:

  1. 2025The Landscape of Agentic Reinforcement Learning for LLMs: A Surveycited by 205
  2. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170
  3. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  4. 2025From Word to World: Can Large Language Models be Implicit Text-based World Models?cited by 42
  5. 2025Agentic Entropy-Balanced Policy Optimizationcited by 40
  6. 2025UserRL: Training Interactive User-Centric Agent via Reinforcement Learningcited by 32
  7. 2025Code2Video: A Code-centric Paradigm for Educational Video Generationcited by 27
  8. 2026Self-Improvements in Modern Agentic Systems: A Surveycited by 19
  9. 2025When Refusals Fail: Unstable Safety Mechanisms in Long-Context LLM Agentscited by 15
  10. 2025Physics Supernova: AI Agent Matches Elite Gold Medalists at IPhO 2025cited by 14
  11. 2025ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Modelscited by 10
  12. 2026Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Usecited by 7
10 more
  1. 2026TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steeringcited by 6
  2. 2026When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer Morecited by 6
  3. 2026Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agentscited by 5
  4. 2025LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledgecited by 3
  5. 2026Beyond the Black Box: Interpretability of Agentic AI Tool Usecited by 3
  6. 2025A Protocol for Trustless Verification Under Uncertaintycited by 2
  7. 2026MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Securitycited by 2
  8. 2026CAP: A Scalable Benchmark for Evaluating Cross-Site Browser Agents with Complex Actions and Perceptioncited by 1
  9. 2026Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commercecited by 0
  10. 2026When Does Correction Become Repair? Mechanistic Auditing of Internal Interventions in Tool-Using LLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026LLM Agents Already Know When to Call Tools -- Even Without Reasoning
  2. 2026Before Agents Decide: Epistemic Action in LLM-Based Systems
  3. 2026Do Agents Know What They Can't Do? Evaluating Feasibility Awareness in Tool-Using Agents
  4. 2026Callability Is Not Operability: Controlled Interface Interventions for LLM Agents
  5. 2026Loud Failures, Quiet Failures: Fault Detection and Recovery in Tool-Using Language Model Agents
  6. 2026ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability
  7. 2026Designing for Doubt: The Case for Informed Abstention in Autonomous Agents
  8. 2026Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict