Tool-Augmented Agentic AI: A Survey on Composition, Selection, and Integration
Abstract
Agentic AI systems enhanced by tool-use represent a shift in how large language models are designed and deployed. They are able to execute tasks that would otherwise be beyond the capabilities of standalone systems. They do this through multi-step reasoning, dynamic interaction with external resources, and by depending on external tools, ranging from APIs to simulators. In this survey, we dive into the rapidly expanding body of research surrounding tool-augmented Agentic AI. This survey examines recent research on tool-augmented agentic AI with a focus on how tools are composed, selected, and integrated into agent architectures. It provides a unifying taxonomy and actionable guidance for navigating this complex landscape. The goal is not to be exhaustive, but to synthesize our findings and relevant frameworks to provide structured guidance for researchers, practitioners and policymakers working on robust, safe and scalable Agentic AI.
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References
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Published
2026-04-09
Section
Special Issue: Advances in Mathematical Sciences
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