- Updated: August 17, 2026
- 2 min read
AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection
AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection
Digital twins are increasingly used to monitor and simulate the behavior of cyber‑physical systems. Even with skilled operators, interpreting anomalies detected within digital twin pipelines is challenging, as the sheer complexity and volume of raw sensor data make thorough analysis difficult. Recent advances in large language models (LLMs) offer promising capabilities for reasoning and explanation, yet their integration into digital‑twin‑driven anomaly analysis remains underexplored.
In this article we present AgenticTwin, an agentic framework that integrates LLM‑driven reasoning with a digital‑twin‑based anomaly detection pipeline. The framework grounds LLM‑generated explanations in outputs from a digital‑twin‑driven anomaly classifier and enables human operators to ask relevant natural‑language questions about the system.
Beyond the framework itself, we introduce a benchmark‑oriented evaluation pipeline constructed over synthetic anomalies injected into a real‑world weather sensor dataset, enabling controlled generation of operator queries over anomaly events. We further evaluate the feasibility of deploying lightweight, open‑source LLMs for practical cyber‑physical environments. Experimental results demonstrate that structured agent collaboration and knowledge‑grounded reasoning improve diagnosis quality, contextual retrieval, and mitigation quality across diverse possible anomaly scenarios.
Read the full paper on arXiv and explore related resources on ubos.tech.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.