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Cited by 77Topic ModelingAI-based Problem Solving and PlanningArtificial Intelligence Applications

Agentic Large Language Models, a Survey

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Plaat, Aske, Van Duijn, Max, Van Stein, Niki, Preuss, Mike, Van der Putten, Peter, Batenburg, Kees Joost·2025·Journal of Artificial Intelligence Research
DOI 10.1613/jair.1.18675
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Abstract

Background: There is great interest in agentic LLMs, large language models that act as agents. Objectives: We review the growing body of work in this area and provide a research agenda. Methods: Agentic LLMs are LLMs that (1) reason, (2) act, and (3) interact. We organize the literature according to these three categories. Results: The research in the first category focuses on reasoning, reflection, and retrieval, aiming to improve decision making; the second category focuses on action models, robots, and tools, aiming for agents that act as useful assistants; the third category focuses on multi-agent systems, aiming for collaborative task solving and simulating interaction to study emergent social behavior. We find that works mutually benefit from results in other categories: retrieval enables tool use, reflection improves multi-agent collaboration, and reasoning benefits all categories. Conclusions: We discuss applications of agentic LLMs and provide an agenda for further research. Important applications are in medical diagnosis, logistics and financial market analysis. Meanwhile, self-reflective agents playing roles and interacting with one another augment the process of scientific research itself. Further, agentic LLMs provide a solution for the problem of LLMs running out of training data: inference-time behavior generates new training states, such that LLMs can keep learning without needing ever larger datasets. We note that there is risk associated with LLM assistants taking action in the real world—safety, liability and security are open problems—while agentic LLMs are also likely to benefit society.

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  • Multi-domain agents engage with tools and/or APIs, manage complex workflows such as coding, healthcare, finance, and crime analysis, and provide memory-based learning that allows them to continue a task by absorbing adaptive information from external cues.

    “Multi-domain agents engage with tools and/or APIs, manage workflows that will inevitably be very complex, such as coding, healthcare, finance, crime analysis, etc., and provide memory-based learning that allows them to continue a task by absorbing adaptive information from external cues [31,67,75-77,136,139].”

    From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [136]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Agentic AI reasoning is temporally continuous, socially collaborative, and learns from its environments.

    “Agentic AI reasoning is temporally continuous, socially collaborative, and learns from its environments [38,136].”

    From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [136]

    Section 1.1 (pp. 3–4) describes agents that reason, communicate, interact with others, and learn from their environments. The Conclusion (p. 52) also discusses social collaboration and learning from feedback. The passages do not establish that agentic AI reasoning is temporally continuous. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • AI Agents, particularly those powered by LLMs, inherit intrinsic limitations that impact their reliability, adaptability, and overall trustworthiness in practical deployments.

    “AI Agents, particularly those powered by LLMs, inherit a number of intrinsic limitations that impact their reliability, adaptability, and overall trustworthiness in practical deployments [187-189].”

    From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [187]

  • AgentHarm Benchmark is used to test robustness against adversarial LLM agent behavior.

    “AgentHarm Benchmark [136] is used to test robustness against adversarial LLM agent behavior.”

    From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [136]

  • Agentic AI systems often struggle with conceptual mistakes, cascading faults, or complex edge cases.

    “Metrics for error recovery remain underdeveloped, and systems often struggle with conceptual mistakes, cascading faults, or complex edge cases [105,136,165].”

    From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [136]

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Authors
Plaat, A., Van Duijn, M., Van Stein, N., Preuss, M., Van der Putten, P., & Batenburg, K. J.
Affiliations
Leiden University (NL)
ORCID
Venue
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Published
2025-12-30
Volume
84
ISSN
1076-9757 (ISSN-L)
DOI
10.1613/jair.1.18675
OpenAlex
W4409318329
Cited by
75
References
0
Citations by year
2026: 68 · 2025: 7
FWCI
30.08
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Artificial Intelligence Research (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
agentic large language models, LLM agents, reasoning, reflection, retrieval-augmented generation, tool use, action models, robots, multi-agent systems, LLM security, diagnosis, financial market analysis

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