Hybrid agentic AI and multi-agent systems in smart manufacturing

The convergence of Agentic Artificial Intelligence (AI) and Multi-Agent Systems (MAS) enables a new paradigm for intelligent decision-making in Smart Manufacturing Systems (SMS). Traditional MAS architectures emphasize distributed coordination and specialized autonomy, while recent advances in agentic AI driven by Large Language Models (LLMs) introduce higher-order reasoning, planning, and tool orchestration capabilities. This paper presents a hybrid agentic AI and multi-agent framework for a Prescriptive Maintenance (RxM) use case, where LLM-based agents provide strategic orchestration and adaptive reasoning, complemented by rule-based and Small Language Models (SLMs) agents performing efficient, domain-specific tasks on the edge. The proposed framework adopts a layered architecture that consists of perception, preprocessing, analytics, and optimization layers, coordinated through an LLM Planner Agent that manages workflow decisions and context retention. Specialized agents autonomously handle schema discovery, intelligent feature analysis, model selection, and prescriptive optimization, while a human-in-the-loop interface ensures transparency and auditability of generated maintenance recommendations. This hybrid approach enables dynamic model adaptation, transparent decision-making, and cost-aware maintenance scheduling based on data-driven insights. An initial proof-of-concept implementation is validated on two industrial manufacturing datasets. The developed framework is modular and extensible, allowing new agents or domain-specific modules to be integrated seamlessly as system capabilities evolve. The results demonstrate the system’s capability to automatically detect schema, adapt preprocessing pipelines, optimize model performance through adaptive intelligence, and generate actionable, prioritized maintenance recommendations. The framework shows promise in achieving improved robustness, scalability, and explainability for RxM in smart manufacturing, bridging the gap between high-level agentic reasoning and low-level autonomous execution. • Hybrid agentic AI and multi-agent framework enables prescriptive maintenance in smart manufacturing. • Layered architecture coordinates perception, preprocessing, analytics, and optimization agents. • LLMs provide strategic reasoning and orchestration, while SLMs support low-latency edge intelligence. • Framework delivers transparent, modular, and cost-aware recommendations with human-in-the-loop oversight. • Validated on manufacturing datasets across classification, regression, and anomaly detection tasks.