# Control Industrial Automation System with Large Language Model Agents

arXiv: https://arxiv.org/abs/2409.18009 · HTML: https://mlsanity.com/paper/2409.18009/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model](https://mlsanity.com/paper/2305.11176/) (2023)
- [Towards autonomous system: flexible modular production system enhanced with large language model agents](https://mlsanity.com/paper/2304.14721/) (2023)
- [Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility](https://mlsanity.com/paper/2407.08550/) (2024)

## Built on by (most cited first, 11 of 11)

- [Autonomous Industrial Control using an Agentic Framework with Large Language Models](https://mlsanity.com/paper/2411.05904/) (2024)
- [Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation](https://mlsanity.com/paper/2507.07115/) (2025)
- [Evaluating Large Language Models for Real-World Engineering Tasks](https://mlsanity.com/paper/2505.13484/) (2025)
- [Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment](https://mlsanity.com/paper/2510.14008/) (2025)
- [Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing](https://mlsanity.com/paper/2506.11180/) (2025)
- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [MaaSO: SLO-aware Orchestration of Heterogeneous Model Instances for MaaS](https://mlsanity.com/paper/2509.06362/) (2025)
- [FacProcessTwin: An LLM-Based System for Process Twin Development](https://mlsanity.com/paper/2606.17666/) (2026)
- [A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents](https://mlsanity.com/paper/2606.31635/) (2026)
- [LLM-Guided Contextual Action Evaluation for Operational Decisions in Industrial Processes](https://mlsanity.com/paper/2608.24156/) (2026)
- [LLM-Driven Multi-Agent Control for Skill-Based Smart Manufacturing](https://mlsanity.com/paper/2610.01364/) (2026)

## Abstract (arXiv)

Traditional industrial automation systems require specialized expertise to operate and complex reprogramming to adapt to new processes. Large language models offer the intelligence to make them more flexible and easier to use. However, LLMs' application in industrial settings is underexplored. This paper introduces a framework for integrating LLMs to achieve end-to-end control of industrial automation systems. At the core of the framework are an agent system designed for industrial tasks, a structured prompting method, and an event-driven information modeling mechanism that provides real-time data for LLM inference. The framework supplies LLMs with real-time events on different context semantic levels, allowing them to interpret the information, generate production plans, and control operations on the automation system. It also supports structured dataset creation for fine-tuning on this downstream application of LLMs. Our contribution includes a formal system design, proof-of-concept implementation, and a method for generating task-specific datasets for LLM fine-tuning and testing. This approach enables a more adaptive automation system that can respond to spontaneous events, while allowing easier operation and configuration through natural language for more intuitive human-machine interaction. We provide demo videos and detailed data on GitHub: https://github.com/YuchenXia/LLM4IAS.