# A Case-Bundle Operating Model for Coding Agents in OpenFOAM-Based CFD

arXiv: https://arxiv.org/abs/2609.11941 · HTML: https://mlsanity.com/paper/2609.11941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 15 Jul 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 8 of 8)

- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [Foam-Agent 2.0: An End-to-End Composable Multi-Agent Framework for Automating CFD Simulation in OpenFOAM](https://mlsanity.com/paper/2509.18178/) (2025)
- [MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing](https://mlsanity.com/paper/2502.00498/) (2025)
- [ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Structured Knowledge and Reasoning](https://mlsanity.com/paper/2506.02019/) (2025)
- [Large language model-empowered next-generation computer-aided engineering](https://mlsanity.com/paper/2509.11447/) (2025)
- [From Perception to Autonomous Computational Modeling: A Multi-Agent Approach](https://mlsanity.com/paper/2604.06788/) (2026)
- [Engineering.ai: A Platform for Teams of AI Engineers in Computational Design](https://mlsanity.com/paper/2511.00122/) (2025)
- [SwarmFoam: An OpenFOAM Multi-Agent System Based on Multiple Types of Large Language Models](https://mlsanity.com/paper/2601.07252/) (2026)

## Abstract (arXiv)

General-purpose coding agents can set up computational fluid dynamics (CFD) cases, execute solvers, and manage remote jobs. Reviewable and reusable work additionally depends on persistent engineering context and evidence. We present a case-bundle operating model with two modes. Build supports agent-assisted case development under engineering review. Replay applies a reviewed case to new variants. We used this model in an OpenFOAM-7 interFoam study for screening injector designs. GPT-5.5 in Codex helped develop a case bundle containing the simulation configuration, geometry-processing and meshing procedures, remote-execution scripts, post-processing code, and review records. The bundle was replayed to execute and post-process 140 Stereolithography (STL) geometry variants on a remote high-performance computing system. A separate replay exercise used the Pi coding agent as the runtime with four different LLM backends. All four runs succeeded and produced verified results. Tool use and token consumption varied across runs. The results show how reviewed case bundles can support bounded, reusable automation with distinct roles for routine execution and engineering judgment.