# CP-Agent: A Harness-Engineered Agent for Crystal Plasticity Simulation Workflows

arXiv: https://arxiv.org/abs/2609.31790 · HTML: https://mlsanity.com/paper/2609.31790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses](https://mlsanity.com/paper/2604.25850/) (2026)
- [Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets](https://mlsanity.com/paper/2604.02460/) (2026)
- [DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation](https://mlsanity.com/paper/2507.14267/) (2025)
- [AI Harness Engineering: A Runtime Substrate for Foundation-Model Software Agents](https://mlsanity.com/paper/2605.13357/) (2026)
- [MooseAgent: A LLM Based Multi-agent Framework for Automating Moose Simulation](https://mlsanity.com/paper/2504.08621/) (2025)
- [Agent-based Learning of Materials Datasets from Scientific Literature](https://mlsanity.com/paper/2312.11690/) (2023)
- [LLM-guided Chemical Process Optimization with a Multi-Agent Approach](https://mlsanity.com/paper/2506.20921/) (2025)
- [ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods](https://mlsanity.com/paper/2603.21011/) (2026)
- [Large language model-empowered next-generation computer-aided engineering](https://mlsanity.com/paper/2509.11447/) (2025)
- [Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey](https://mlsanity.com/paper/2401.14043/) (2024)
- [Multi-Agent Orchestration for High-Throughput Materials Screening on a Leadership-Class System](https://mlsanity.com/paper/2604.07681/) (2026)
- [Aethorix v1.0: An Integrated Scientific AI Agent for Scalable Inorganic Materials Innovation and Industrial Implementation](https://mlsanity.com/paper/2506.16609/) (2025)
- [A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models](https://mlsanity.com/paper/2511.17511/) (2025)
- [From Data to Theory: Autonomous Large Language Model Agents for Materials Science](https://mlsanity.com/paper/2604.19789/) (2026)

## Abstract (arXiv)

Crystal plasticity (CP) simulations predict the mechanical behavior of polycrystalline metals, yet their routine use is hindered by the manual effort of configuring heterogeneous tools, orchestrating multi-step data pipelines, and calibrating constitutive parameters against experiments. These bottlenecks impede productivity in systematic parameter studies, motivating interest in automated workflows. This study presents CP-Agent, a harness-engineered LLM-based agent that autonomously executes complete CP modeling workflows from natural-language tasks. Operating under the ReAct paradigm, the agent reasons about tool selection and sequencing while delegating numerical search to established optimizers. The harness comprises a minimal system prompt, typed tool definitions, a dispatcher, and a safety-bounded iteration loop, encoding domain knowledge through tool schemas rather than hard-coded logic. CP-Agent is demonstrated on four case studies: calibrating four slip parameters of additively manufactured stainless steel 316L against tensile data; validating the workflow against published copper benchmarks, reproducing stress-strain and texture evolution; recovering the initial crystallographic texture of copper, where the agent correctly identifies a diffuse initial texture; and reproducing the multi-pass rolling texture evolution of a Mg-Zn-Ca alloy, where the agent chains five deformation passes and recovers the experimentally observed weakened, split basal texture. In all cases, the agent inferred the correct execution sequence from the task statement, robustly across repeated runs, and delivered physically interpretable results. This work establishes harness engineering as a systematic approach to automating CP modeling workflows while maintaining physical interpretability and auditability through visible reasoning traces.