EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design
EngiAI introduces a multi-agent framework and benchmark suite designed for LLM-driven engineering design tasks. The framework includes various evaluation dimensions, such as workflow benchmarks and retrieval-augmented generation assessments. Results indicate significant task completion rates for proprietary models, while highlighting challenges in conditional branching tasks.
- ▪EngiAI is a multi-agent system that operationalizes a benchmark for LLM-driven engineering design.
- ▪The benchmark suite evaluates workflows, retrieval contributions, and HPC orchestration.
- ▪Proprietary models achieved 96-97% average task completion, while open-source models reached 55-78%.
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Computer Science > Artificial Intelligence arXiv:2605.19743 (cs) [Submitted on 19 May 2026] Title:EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design Authors:Gioele Molinari, Florian Felten, Soheyl Massoudi, Mark Fuge View a PDF of the paper titled EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design, by Gioele Molinari and 3 other authors View PDF HTML (experimental) Abstract:Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address multi-agent systems that combine simulation, retrieval, and manufacturing preparation.
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