Citation :

Abdinasir Ismael Hashi, Abdirizak Mohamed Hashi, Osman Abdullahi Jama, Ibrahim Rashid Abdullahi, "Decentralized Agentic AI Orchestration for Autonomous Self-Healing and Resilience in Distributed Cyber-Physical Systems," International Journal of Computer Trends and Technology (IJCTT), vol. 74, no. 4, pp. 10-28, 2026. Crossref, https://doi.org/10.14445/22312803/IJCTT-V74I4P102

Abstract

The study explores a decentralized agentic artificial intelligence orchestration paradigm for enhancing selfrecovery and resilience within a distributed CPS. The traditional paradigms, characterized by their centralized nature, have been observed to present constraints like low scalability, delays in reaction, and susceptibility to single-point failures. As a result, the study has proven to be unsuitable for application in dynamic and complex settings. To solve these problems, a decentralized agentic AI framework is proposed, which utilizes autonomous agents with deep learning models, such as CNN, RNN, and the combined CNN-RNN model for anomaly detection and recovery. According to experimental findings, while the CNN and RNN models exhibit perfect recall performance (1.0000), the study further exhibits high false positives and low reliability scores. Conversely, the hybrid model performs far better than the individual models with regard to F1-score (0.9863), almost perfect AUC score, reduced false positive rates (0.0476), and reliability levels. Despite its relatively high detection latency compared to the individual models, the hybrid framework offers the best trade-off between precision, reliability, and stability.

Keywords

Decentralized Agentic AI, Cyber-Physical Systems, Anomaly Detection, Hybrid CNN-RNN, System Resilience, Self-Healing Systems.

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