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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.
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Large Language Model based Multi-Agents: A Survey of Progress and Challenges.
Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Due to the impressive planning and reasoning abilities of LLMs, they have been used as autonomous agents to do many tasks automatically. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation. To provide the community with an overview of this dynamic field, we present this survey to offer an in-depth discussion on the essential aspects of multi-agent systems based on LLMs, as well as the challenges. Our goal is for readers to gain substantial insights on the following questions: What domains and environments do LLM-based multi-agents simulate? How are these agents profiled and how do they communicate? What mechanisms contribute to the growth of agents' capacities? For those interested in delving into this field of study, we also summarize the commonly used datasets or benchmarks for them to have convenient access. To keep researchers updated on the latest studies, we maintain an open-source GitHub repository, dedicated to outlining the research on LLM-based multi-agent systems.
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- Award ID(s):
- 2202693
- PAR ID:
- 10508149
- Publisher / Repository:
- IJCAI ; Cornell arxiv
- Date Published:
- Journal Name:
- 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024)
- Format(s):
- Medium: X
- Location:
- South Korea
- Sponsoring Org:
- National Science Foundation
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