Agentic Large Language Models, a Survey
Authors: Aske Plaat, Max van Duijn, Niki van Stein, Mike Preuss, Peter van der Putten, Kees Joost Batenburg
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 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. |
| Researcher Affiliation | Academia | ASKE PLAAT , Leiden University, Netherlands MAX VAN DUIJN, Leiden University, Netherlands NIKI VAN STEIN, Leiden University, Netherlands MIKE PREUSS, Leiden University, Netherlands PETER VAN DER PUTTEN, Leiden University & AI Lab, Pegasystems, Netherlands KEES JOOST BATENBURG, Leiden University, Netherlands |
| Pseudocode | Yes | Figure 9 provides pseudo-code for the algorithm, in which the three calls to the LLM are clearly shown. |
| Open Source Code | No | The paper is a survey and does not provide explicit open-source code for its own methodology. It mentions open-source models and tools that others have developed. |
| Open Datasets | No | This paper is a survey of existing literature and does not introduce or provide access to a new dataset for its own methodology. It discusses various datasets and benchmarks used by the papers it surveys. |
| Dataset Splits | No | This paper is a survey and does not conduct experiments with dataset splits. It describes methodologies and findings from other research papers. |
| Hardware Specification | No | This paper is a survey and does not describe the specific hardware used to run its own experiments. It mentions hardware in the context of other surveyed papers, but not for its own methodology. |
| Software Dependencies | No | This paper is a survey and does not specify software dependencies with version numbers for its own methodology. It discusses various software tools and frameworks used in the surveyed literature. |
| Experiment Setup | No | This paper is a survey and does not detail an experimental setup with hyperparameters or system-level training settings for its own methodology. Its methodology is primarily literature review and taxonomy creation. |