International Journal of Technology and Applied Science

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Universal Adaptive Multi-Agent Coordination (AMAC): A Domain-Agnostic Deep Learning Framework for Autonomous Detection and Response Across Industry Sectors

Author(s) Mr. Lalith Chandra Bandaru
Country United States
Abstract Most intelligent monitoring and response systems are built for a single domain — a fraud detection model serves finance, a network intrusion detector serves cybersecurity, a patient early-warning system serves clinical care. Each is redesigned from scratch when requirements change, and insights gained in one field rarely propagate to adjacent communities facing structurally identical problems. This paper challenges that fragmentation. We present Universal AMAC — Adaptive Multi-Agent Coordination — a domain-agnostic framework in which four role-specialized agents connected through a differentiable adaptive message-passing mechanism can be deployed across arbitrary application sectors by retraining only a lightweight domain-specific layer. Evaluated across five structurally distinct domains — cybersecurity, clinical healthcare, financial fraud detection, industrial IoT anomaly detection, and smart energy grid management — Universal AMAC demonstrates competitive or superior performance relative to both single-agent baselines and domain-specific multi-agent methods in each sector. Cross-domain transfer experiments confirm that adapting a pretrained AMAC model to a new sector requires fewer than fifty fine-tuning epochs, making the framework a practical foundation for any organisation seeking intelligent multi-domain automation without the cost of maintaining independent systems per sector.
Keywords multi-agent systems, domain adaptation, large language models, emergent communication, deep reinforcement learning, cybersecurity, healthcare AI, fraud detection, industrial IoT, smart grid, transfer learning
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January 2026
Published On 2026-01-11
DOI https://doi.org/10.71097/IJTAS.v17.i1.1327
Short DOI https://doi.org/hb55q9
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