A Systematic Literature Review of Agentic AI: Definitions, Architectures, and Challenges
Cassiano Moralles, Luis Antonio L. F. Da Costa, Sandro José Rigo, Rafael Kunst, Vinícius Costa Souza, Ederson Passos Silva, George Lucas Ebertz Prado, Taimisson De Carvalho Schardosim, Alex Roehrs · IEEE Access · 2026
This Systematic Literature Review (SLR) synthesizes the rapidly evolving state of Agentic AI, analyzing peer-reviewed studies published between 2021 and 2025. We organize the current body of knowledge into a taxonomy comprising five distinct research fields: Memory Cognition, Networking Systems, Trust Safety, Evaluation Limits, and Applications Use-Cases. Our intersection analysis reveals a research landscape currently in an "exploitation" phase, heavily skewed toward Applications Use-Cases (31.9%) and the practical implementation of Autonomy. While the community is successfully operationalizing existing autonomous capabilities, we identify a critical "research desert" regarding Adaptability and New Models. The findings indicate a scarcity of architectural frameworks designed to support long-term resilience and dynamic behavior modification in unforeseen environments. To advance Agentic AI from brittle prototypes to robust real-world systems, we conclude that future research must pivot from solely demonstrating autonomy toward engineering adaptability and developing novel modeling paradigms that prioritize responsiveness to change.