AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges
Cornell University · University of Peloponnese
Abstract
Information fusion, in the context of the Generative AI era, must distinguish AI Agents from Agentic AI. This review critically distinguishes between AI Agents and Agentic AI, offering a structured, conceptual taxonomy, application mapping, and analysis of opportunities and challenges to clarify their divergent design philosophies and capabilities. We begin by outlining the search strategy and foundational definitions, characterizing AI Agents as modular systems driven and enabled by LLMs and LIMs for task-specific automation. Generative AI is positioned as a precursor providing the foundation, with AI agents advancing through tool integration, prompt engineering, and reasoning enhancements. We then…
Citation impact
- FWCI
- 172.30
- Percentile
- 100%
- References
- 246
Authors
3Topics & keywords
- Taxonomy (biology)
- Computer science
- Data science
- Cognitive science
- Artificial intelligence
- Psychology
- Ecology