# Autonomous Industrial Control using an Agentic Framework with Large Language Models

arXiv: https://arxiv.org/abs/2411.05904 · HTML: https://mlsanity.com/paper/2411.05904/

## Key facts

- Cited by 19 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 20 of its 36 references that are in the map.
- First posted on arXiv 8 Nov 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, 20 of 20)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Language Models can Solve Computer Tasks](https://mlsanity.com/paper/2303.17491/) (2023)
- [Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects](https://mlsanity.com/paper/2401.03428/) (2024)
- [Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling](https://mlsanity.com/paper/2305.09993/) (2023)
- [LLMatDesign: Autonomous Materials Discovery with Large Language Models](https://mlsanity.com/paper/2406.13163/) (2024)
- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Towards autonomous system: flexible modular production system enhanced with large language model agents](https://mlsanity.com/paper/2304.14721/) (2023)
- [Diagnosing Infeasible Optimization Problems Using Large Language Models](https://mlsanity.com/paper/2308.12923/) (2023)
- [Pre-Trained Large Language Models for Industrial Control](https://mlsanity.com/paper/2308.03028/) (2023)
- [RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents](https://mlsanity.com/paper/2406.11132/) (2024)
- [GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction](https://mlsanity.com/paper/2310.03030/) (2023)
- [Toward automatic generation of control structures for process flow diagrams with large language models](https://mlsanity.com/paper/2211.05583/) (2022)
- [An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems](https://mlsanity.com/paper/2307.11432/) (2023)
- [Control Industrial Automation System with Large Language Model Agents](https://mlsanity.com/paper/2409.18009/) (2024)

## Built on by (most cited first, 19 of 19)

- [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025)
- [LLM-guided Chemical Process Optimization with a Multi-Agent Approach](https://mlsanity.com/paper/2506.20921/) (2025)
- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Multi-agent systems for chemical engineering: A review and perspective](https://mlsanity.com/paper/2508.07880/) (2025)
- [Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation](https://mlsanity.com/paper/2507.07115/) (2025)
- [Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing](https://mlsanity.com/paper/2506.11180/) (2025)
- [A Systematic Approach to Multi-Agent AI from Advanced Regulatory Control Theory: Safe and Auditable LLM Operator Agents for Process Control](https://mlsanity.com/paper/2606.30877/) (2026)
- [Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models](https://mlsanity.com/paper/2603.24629/) (2026)
- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industry](https://mlsanity.com/paper/2507.15268/) (2025)
- [Sensory-Motor Control with Large Language Models via Iterative Policy Refinement](https://mlsanity.com/paper/2506.04867/) (2025)
- [Agentic Meta-Orchestrator for Multi-task Copilots](https://mlsanity.com/paper/2510.22781/) (2025)
- [Multi-Modal Agents for Power Distribution Defect Detection: An Evaluation of Foundation Models](https://mlsanity.com/paper/2606.12969/) (2026)
- [SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets](https://mlsanity.com/paper/2607.08681/) (2026)
- [Closed-Loop Control with Rule-Aligned Small Language Models and Multi-Agent Self-Correction](https://mlsanity.com/paper/2607.09713/) (2026)
- [An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models](https://mlsanity.com/paper/2607.21292/) (2026)
- [ADMITBench: A Safety-Governed Reference Framework for Evaluating the Admissibility of Industrial LLM Advisories](https://mlsanity.com/paper/2608.03866/) (2026)
- [AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection](https://mlsanity.com/paper/2608.11679/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

As chemical plants evolve towards full autonomy, the need for effective fault handling and control in dynamic, unpredictable environments becomes increasingly critical. This paper proposes an innovative approach to industrial automation, introducing validation and reprompting architectures utilizing large language model (LLM)-based autonomous control agents. The proposed agentic system, comprising of operator, validator, and reprompter agents, enables autonomous management of control tasks, adapting to unforeseen disturbances without human intervention. By utilizing validation and reprompting architectures, the framework allows agents to recover from errors and continuously improve decision-making in real-time industrial scenarios. We hypothesize that this mechanism will enhance performance and reliability across a variety of LLMs, offering a path toward fully autonomous systems capable of handling unexpected challenges, paving the way for robust, adaptive control in complex industrial environments. To demonstrate the concept's effectiveness, we created a simple case study involving a temperature control experiment embedded on a microcontroller device, validating the proposed approach.