# Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents

arXiv: https://arxiv.org/abs/2410.14209 · HTML: https://mlsanity.com/paper/2410.14209/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 70 references that are in the map.
- First posted on arXiv 18 Oct 2024.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 30)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [MapCoder: Multi-Agent Code Generation for Competitive Problem Solving](https://mlsanity.com/paper/2405.11403/) (2024)
- [AutoSafeCoder: A Multi-Agent Framework for Securing LLM Code Generation through Static Analysis and Fuzz Testing](https://mlsanity.com/paper/2409.10737/) (2024)
- [LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems](https://mlsanity.com/paper/2401.05443/) (2024)
- [Automated C/C++ Program Repair for High-Level Synthesis via Large Language Models](https://mlsanity.com/paper/2407.03889/) (2024)
- [Supersonic: Learning to Generate Source Code Optimizations in C/C++](https://mlsanity.com/paper/2309.14846/) (2023)
- [FRANC: A Lightweight Framework for High-Quality Code Generation](https://mlsanity.com/paper/2307.08220/) (2023)
- [Generalizing to New Domains by Mapping Natural Language to Lifted LTL](https://mlsanity.com/paper/2110.05603/) (2021)
- [LLM-based Control Code Generation using Image Recognition](https://mlsanity.com/paper/2311.10401/) (2023)
- [ChatGPT for PLC/DCS Control Logic Generation](https://mlsanity.com/paper/2305.15809/) (2023)
- [K-ST: A Formal Executable Semantics of the Structured Text Language for PLCs](https://mlsanity.com/paper/2202.04076/) (2022)
- [Generating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants](https://mlsanity.com/paper/2402.08431/) (2024)
- [Automated Control Logic Test Case Generation using Large Language Models](https://mlsanity.com/paper/2405.01874/) (2024)

## Built on by (most cited first, 26 of 26)

- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [A Survey on Trustworthy LLM Agents: Threats and Countermeasures](https://mlsanity.com/paper/2503.09648/) (2025)
- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)
- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [InCoder-32B-Thinking: Industrial Code World Model for Thinking](https://mlsanity.com/paper/2604.03144/) (2026)
- [AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers](https://mlsanity.com/paper/2412.02410/) (2024)
- [Agent-as-a-Service based on Agent Network](https://mlsanity.com/paper/2505.08446/) (2025)
- [EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing](https://mlsanity.com/paper/2508.13003/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation](https://mlsanity.com/paper/2502.16529/) (2025)
- [Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces](https://mlsanity.com/paper/2505.03295/) (2025)
- [Beyond touch-based human-machine interface: Control your machines in natural language by utilizing large language models and OPC UA](https://mlsanity.com/paper/2510.11300/) (2025)
- [Vendor-Aware Industrial Agents: RAG-Enhanced LLMs for Secure On-Premise PLC Code Generation](https://mlsanity.com/paper/2511.09122/) (2025)
- [From Thinker to Society: Security in Hierarchical Autonomy Evolution of AI Agents](https://mlsanity.com/paper/2603.07496/) (2026)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)
- [Lifecycle-Aware code generation: Leveraging Software Engineering Phases in LLMs](https://mlsanity.com/paper/2510.24019/) (2025)
- [LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment](https://mlsanity.com/paper/2601.19487/) (2026)
- [Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2606.31614/) (2026)
- [RAG-Based Auto-Configuration for Industrial Fieldbus Devices](https://mlsanity.com/paper/2608.08618/) (2026)
- [AI Control Scientist: LLM-driven Agentic System for Automated Control Design](https://mlsanity.com/paper/2608.26780/) (2026)
- [PLCBench: Can Autonomous LLM Agents Turn PLC Access into Sustained Physical Impact?](https://mlsanity.com/paper/2608.26882/) (2026)
- [Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems](https://mlsanity.com/paper/2609.16599/) (2026)
- [Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority](https://mlsanity.com/paper/2609.30219/) (2026)
- [From Dead Code and Static Requirements to Working Engines: Software Revival with Coding Agents](https://mlsanity.com/paper/2609.36161/) (2026)
- [PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulation](https://mlsanity.com/paper/2610.02982/) (2026)

## Abstract (arXiv)

In industrial control systems, the generation and verification of Programmable Logic Controller (PLC) code are critical for ensuring operational efficiency and safety. While Large Language Models (LLMs) have made strides in automated code generation, they often fall short in providing correctness guarantees and specialized support for PLC programming. To address these challenges, this paper introduces Agents4PLC, a novel framework that not only automates PLC code generation but also includes code-level verification through an LLM-based multi-agent system. We first establish a comprehensive benchmark for verifiable PLC code generation area, transitioning from natural language requirements to human-written-verified formal specifications and reference PLC code. We further enhance our `agents' specifically for industrial control systems by incorporating Retrieval-Augmented Generation (RAG), advanced prompt engineering techniques, and Chain-of-Thought strategies. Evaluation against the benchmark demonstrates that Agents4PLC significantly outperforms previous methods, achieving superior results across a series of increasingly rigorous metrics. This research not only addresses the critical challenges in PLC programming but also highlights the potential of our framework to generate verifiable code applicable to real-world industrial applications.