Agentic Artificial Intelligence for Information Fusion

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Organizations across healthcare, enterprise analytics, supply chains, and cybersecurity struggle to integrate heterogeneous data sources for timely, reliable decision-making. Traditional information fusion relies on static, pipeline-oriented architectures inadequate for dynamic, distributed, and real-time environments. This study presents a PRISMA-guided systematic review integrating agentic decision theory with organizational information systems perspectives, including the Technology Acceptance Model, Task-Technology Fit, and Sociotechnical Systems Theory. A search of four databases yielded 1,205 records, from which 380 unique publications were identified. A six-dimension taxonomy covering architectures, learning paradigms, coordination mechanisms, decision coupling, trust modeling, and uncertainty representation is developed. Hybrid cloud-edge and federated architectures with multi-agent reinforcement learning offer favorable scalability and robustness trade-offs. Bias-aware design, human-in-the-loop accountability, and explainable outputs are necessary for responsible deployment.