# Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations

arXiv: https://arxiv.org/abs/2603.12813 · HTML: https://mlsanity.com/paper/2603.12813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 6 of 6)

- [Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks](https://mlsanity.com/paper/2207.12051/) (2022)
- [SFILES 2.0: An extended text-based flowsheet representation](https://mlsanity.com/paper/2208.00778/) (2022)
- [Deep reinforcement learning for process design: Review and perspective](https://mlsanity.com/paper/2308.07822/) (2023)
- [Multi-agent systems for chemical engineering: A review and perspective](https://mlsanity.com/paper/2508.07880/) (2025)
- [Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)](https://mlsanity.com/paper/2503.17004/) (2025)
- [Large Language Model Agent for User-friendly Chemical Process Simulations](https://mlsanity.com/paper/2601.11650/) (2026)

## Built on by (most cited first, 2 of 2)

- [Post-Edit Re-Verification in Simulator-Backed Engineering Agents: A Controlled Comparison of Verification-Cadence Guidance](https://mlsanity.com/paper/2608.28147/) (2026)
- [Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering](https://mlsanity.com/paper/2610.02014/) (2026)

## Abstract (arXiv)

Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In this work, we present an agentic AI framework that delivers assistance in an industrial flowsheet simulation environment. To this end, we show the capabilities of GitHub Copilot (GitHub, Inc., 2026), when using state-of-the-art LLMs, such as Claude Opus 4.6 (Anthropic, PBC, 2026), to generate valid syntax for our in-house process modelling tool Chemasim using the technical documentation and a few commented examples as context. Based on this, we develop a multi-agent system that decomposes process development tasks with one agent solving the abstract problem using engineering knowledge and another agent implementing the solution as Chemasim code. We demonstrate the effectiveness of our framework for typical flowsheet modelling examples, including (i) a reaction/separation process, (ii) a pressure-swing distillation, and (iii) a heteroazeotropic distillation including entrainer selection. Along these lines, we discuss current limitations of the framework and outline future research directions to further enhance its capabilities.