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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM62009

AGENTIC AI-BASED ANOMALY DETECTION AND DECISION SYSTEM

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62009

Volume:

10 |

4 |

April 2026

Pages:

1-3

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MRR

Ms. Rithanyaa R

Artificial Intelligence and Machine Learning

Sri Shakthi Institute of Engineering and Technology

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MPL

Mr. Prajeeth LS

Artificial Intelligence and Machine Learning

Sri Shakthi Institute of Engineering and Technology

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MRM

Mr. Rohith M

Artificial Intelligence and Machine Learning

Sri Shakthi Institute of Engineering and Technology

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MLK

Mr. Lalithkishore K

Artificial Intelligence and Machine Learning

Sri Shakthi Institute of Engineering and Technology

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Mentors

MSR

Mr. Sabarinath R

Artificial Intelligence and Machine Learning

Sri Shakthi Institute of Engineering and Technology

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Abstract

In today’s data-driven world, the need for intelligent systems capable of automatically detecting anomalies and making decisions has become increasingly important. This project presents an Agentic AI-Based Anomaly Detection and Intelligent Decision System designed to monitor data, identify abnormal patterns, and provide real-time decision support. The system integrates machine learning and deep learning techniques, particularly LSTM, to analyze sequential data and detect anomalies efficiently. An agent-based architecture is implemented where multiple agents collaborate to evaluate system states and take appropriate actions. Additionally, a language-based component is incorporated to generate human-readable explanations, improving system interpretability. The proposed system is visualized through an interactive dashboard that displays metrics, alerts, and decisions in real time. The system provides a scalable and efficient solution for intelligent monitoring and automated decision-making.

KEYWORDS

Key Words: Agentic AI Anomaly Detection LSTM Machine Learning Deep Learning Intelligent Systems Dashboard LLM

Cite This Article

Ms. Rithanyaa R, Mr. Prajeeth LS, Mr. Rohith M, Mr. Lalithkishore K, Mr. Sabarinath R, "AGENTIC AI-BASED ANOMALY DETECTION AND DECISION SYSTEM," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-3, April 2026.

Full Article/Conclusion

The proposed Agentic AI-Based Anomaly Detection System provides an effective solution for intelligent data monitoring and decision-making. By integrating machine learning, deep learning, agent systems, and language models, the system achieves accurate anomaly detection and automated response generation. The system is scalable, adaptable, and suitable for real-time applications. Future improvements may include real-time data integration, advanced models, and enhanced agent collaboration.

References

·        Chandola, V., Banerjee, A., & Kumar, V., “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, 2009. ·        Malhotra, P., Vig, L., Shroff, G., & Agarwal, P., “Long Short-Term Memory Networks for Anomaly Detection in Time Series,” Proceedings of ESANN, 2015. ·        Liu, F. T., Ting, K. M., & Zhou, Z. H., “Isolation Forest,” IEEE International Conference on Data Mining, 2008. ·        Sakurada, M., & Yairi, T., “Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction,” Proceedings of MLSDA, 2014. ·        Chalapathy, R., & Chawla, S., “Deep Learning for Anomaly Detection: A Survey,” arXiv preprint arXiv:1901.03407, 2019. ·        Vaswani, A., et al., “Attention Is All You Need,” Advances in Neural Information Processing Systems (NeurIPS), 2017. ·        Radford, A., et al., “Improving Language Understanding by Generative Pre-Training,” OpenAI Technical Report, 2018. ·        Wooldridge, M., “An Introduction to MultiAgent Systems,” John Wiley & Sons, 2009. ·        Ribeiro, M. T., Singh, S., & Guestrin, C., “Why Should I Trust You? Explaining the Predictions of Any Classifier,” ACM SIGKDD, 2016. ·        Busoniu, L., Babuska, R., & De Schutter, B., “A Comprehensive Survey of Multi-Agent Reinforcement Learning,” IEEE Transactions on

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Publication Date

4/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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