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Related Experiment Videos

Agentic and LLM-Based Multimodal Anomaly Detection: Architectures, Challenges, and Prospects.

Mohammed Ayalew Belay1, Amirshayan Haghipour1, Adil Rasheed2

  • 1Department of Electronic Systems, Norwegian University of Science and Technology, 7034 Trondheim, Norway.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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This review explores agentic artificial intelligence and large language models for multimodal anomaly detection. It introduces a new taxonomy unifying these methods and highlights future research directions for enhanced system reliability.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Anomaly detection is vital for complex systems (e.g., industrial, cybersecurity, autonomous).
  • Multimodal anomaly detection is increasingly applied in dynamic environments, moving beyond single data modalities.

Purpose of the Study:

  • To comprehensively review research at the intersection of agentic AI and large language-model-based multimodal anomaly detection.
  • To propose a novel taxonomy unifying agentic and multimodal anomaly detection methods.

Main Methods:

  • Systematic analysis and categorization of existing studies based on agent architecture, reasoning, tool integration, and modality scope.
  • Identification of benchmark datasets, evaluation methods, challenges, and mitigation strategies.
Keywords:
agentic AIagentic anomaly detectionagentscross-modal fusionlarge language modelsmultimodal

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Main Results:

  • A novel taxonomy is presented, unifying agentic and multimodal anomaly detection.
  • Key challenges such as data alignment, scalability, reliability, explainability, and evaluation standardization are identified.

Conclusions:

  • Future research should focus on trustworthy autonomous agents, efficient multimodal fusion, and human-in-the-loop systems.
  • Emphasis is placed on real-world deployment in safety-critical applications for anomaly detection.