# Towards autonomous system: flexible modular production system enhanced with large language model agents

arXiv: https://arxiv.org/abs/2304.14721 · HTML: https://mlsanity.com/paper/2304.14721/

## Key facts

- Cited by 38 papers in the map (data through 7 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 28 Apr 2023.

## 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, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Analyzing the Structure of Attention in a Transformer Language Model](https://mlsanity.com/paper/1906.04284/) (2019)
- [Why think step by step? Reasoning emerges from the locality of experience](https://mlsanity.com/paper/2304.03843/) (2023)
- [Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models](https://mlsanity.com/paper/2107.03324/) (2021)
- [An Agent-based Realisation for a continuous Model Adaption Approach in intelligent Digital Twins](https://mlsanity.com/paper/2212.03681/) (2022)

## Built on by (most cited first, 30 of 38)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [On Generative Agents in Recommendation](https://mlsanity.com/paper/2310.10108/) (2023)
- [MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow](https://mlsanity.com/paper/2503.18968/) (2025)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects](https://mlsanity.com/paper/2504.19838/) (2025)
- [LLMind: Orchestrating AI and IoT with LLM for Complex Task Execution](https://mlsanity.com/paper/2312.09007/) (2023)
- [Autonomous Industrial Control using an Agentic Framework with Large Language Models](https://mlsanity.com/paper/2411.05904/) (2024)
- [Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications](https://mlsanity.com/paper/2406.10300/) (2024)
- [LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins](https://mlsanity.com/paper/2405.18092/) (2024)
- [Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents](https://mlsanity.com/paper/2311.13148/) (2023)
- [An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems](https://mlsanity.com/paper/2403.16809/) (2024)
- [Control Industrial Automation System with Large Language Model Agents](https://mlsanity.com/paper/2409.18009/) (2024)
- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0](https://mlsanity.com/paper/2403.17209/) (2024)
- [Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation](https://mlsanity.com/paper/2507.07115/) (2025)
- [Large Language Models as Minecraft Agents](https://mlsanity.com/paper/2402.08392/) (2024)
- [Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility](https://mlsanity.com/paper/2407.08550/) (2024)
- [A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model](https://mlsanity.com/paper/2408.02920/) (2024)
- [An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework](https://mlsanity.com/paper/2504.14681/) (2025)
- [Simulation Agent: A Framework for Integrating Simulation and Large Language Models for Enhanced Decision-Making](https://mlsanity.com/paper/2505.13761/) (2025)
- [Chain-of-Trigger: An Agentic Backdoor that Paradoxically Enhances Agentic Robustness](https://mlsanity.com/paper/2510.08238/) (2025)
- [Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol](https://mlsanity.com/paper/2512.03955/) (2025)
- [Knowledge Sharing in Manufacturing using Large Language Models: User Evaluation and Model Benchmarking](https://mlsanity.com/paper/2401.05200/) (2024)
- [Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review](https://mlsanity.com/paper/2505.00210/) (2025)
- [Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning](https://mlsanity.com/paper/2505.13994/) (2025)
- [Hyperbolic Large Language Models](https://mlsanity.com/paper/2509.05757/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies](https://mlsanity.com/paper/2410.13247/) (2024)
- [Integrating Machine Learning into Belief-Desire-Intention Agents: Current Advances and Open Challenges](https://mlsanity.com/paper/2510.20641/) (2025)

## Abstract (arXiv)

In this paper, we present a novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes. We retrofit the automation system for a modular production facility and create executable control interfaces of fine-granular functionalities and coarse-granular skills. Low-level functionalities are executed by automation components, and high-level skills are performed by automation modules. Subsequently, a digital twin system is developed, registering these interfaces and containing additional descriptive information about the production system. Based on the retrofitted automation system and the created digital twins, LLM-agents are designed to interpret descriptive information in the digital twins and control the physical system through service interfaces. These LLM-agents serve as intelligent agents on different levels within an automation system, enabling autonomous planning and control of flexible production. Given a task instruction as input, the LLM-agents orchestrate a sequence of atomic functionalities and skills to accomplish the task. We demonstrate how our implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. This research highlights the potential of integrating LLMs into industrial automation systems in the context of smart factory for more agile, flexible, and adaptive production processes, while it also underscores the critical insights and limitations for future work. Demos at: https://github.com/YuchenXia/GPT4IndustrialAutomation