Agentic Tool Use in Large Language Models (April 2026)

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Comprehensive framework for understanding tool use in agentic systems — schema understanding, calling conventions, error handling, tool composition patterns

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Open Source

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About Agentic Tool Use in Large Language Models (April 2026)

Agentic Tool Use in Large Language Models is a comprehensive survey paper that systematically organizes the literature on how LLMs leverage external tools for autonomous agent tasks. The paper identifies three key paradigms: (1) prompting as plug-and-play, (2) supervised tool learning, and (3) reward-driven tool policy learning. It analyzes methods, strengths, and failure modes across these paradigms, reviews the evaluation landscape, and highlights open challenges. Aimed at researchers and practitioners, it provides a structured evolutionary view of tool-use methods in LLM-based agents.

Key Features

Taxonomy of three paradigms: prompting as plug-and-play, supervised tool learning, and reward-driven tool policy learning

Analysis of methods, strengths, and failure modes across paradigms

Review of evaluation benchmarks and metrics for tool-use capabilities

Identification of key challenges and future research directions

Unified view of fragmented tool-use literature

Pros & Cons

Pros

Provides a clear taxonomy of tool-use approaches
Covers both prompting and learning-based methods
Discusses failure modes and limitations in detail
Includes evaluation landscape review

Cons

Not a deployable software tool; it is a research survey paper
May not reflect the most recent developments after June 2026
Not peer-reviewed; hosted on arXiv

Best For

Research on LLM agent tool integrationUnderstanding trade-offs between training-based and prompting-based tool useDesigning evaluation frameworks for tool-use agentsIdentifying gaps in current tool-use methods

FAQ

What is the main contribution of this paper?

It organizes the fragmented literature on LLM tool use into three structured paradigms — prompting as plug-and-play, supervised tool learning, and reward-driven tool policy learning — and analyzes their methods, strengths, failure modes, evaluation, and challenges.

How many paradigms of tool use are identified?

Three: prompting-based, supervised learning-based, and reward-driven policy learning.

Is this paper peer-reviewed?

No, it is a preprint on arXiv and has not undergone formal peer review.