mlsanityData to 14 Oct 2026

DUCTILE: Agentic LLM Orchestration of Engineering Analysis in Product Development Practice

arXiv 2603.10249 ↗First posted 10 Mar 2026cs.SE cs.AI cs.HCcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Engineering analysis automation in product development relies on rigid interfaces between tools, data formats and documented processes. When these interfaces change, as they routinely do as the product evolves in the engineering ecosystem, the automation support breaks. This paper presents a DUCTILE (Delegated, User-supervised Coordination of Tool- and document-Integrated LLM-Enabled) agentic orchestration, an approach for developing, executing and evaluating LLM-based agentic automation support of engineering analysis tasks. The approach separates adaptive orchestration, performed by the LLM agent, from deterministic execution, performed by verified engineering tools. The agent interprets documented design practices, inspects input data and adapts the processing path, while the engineer supervises and exercises final judgment. DUCTILE is demonstrated on an industrial structural analysis task at an aerospace manufacturer, where the agent handled input deviations in format, units, naming conventions and methodology that would break traditional scripted pipelines. Evaluation against expert-defined acceptance criteria and deployment with practicing engineers confirm that the approach produces correct, methodologically compliant results across 10 repeated independent runs. The paper discusses the paradigm shift and the practical consequences of adopting agentic automation, including unintended effects on the nature of engineering work when removing mundane tasks and creating an exhausting supervisory role.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 10 of its 90 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

10 of 90 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  4. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,986
  5. 2025Mercury: Ultra-Fast Language Models Based on Diffusioncited by 235
  6. 2021Deep Generative Models in Engineering Design: A Reviewcited by 105
  7. 2023From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Designcited by 51
  8. 2024SPRIG: Improving Large Language Model Performance by System Prompt Optimizationcited by 29
  9. 2021Towards Reusable Surrogate Models: Graph-Based Transfer Learning on Trussescited by 7
  10. 2025EngiBench: A Framework for Data-Driven Engineering Design Researchcited by 6

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  1. 2025Automating Structural Engineering Workflows with Large Language Model Agents
  2. 2026Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities
  3. 2024Agentless: Demystifying LLM-based Software Engineering Agents
  4. 2026EngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions
  5. 2026StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows
  6. 2026Evaluating LLM Agents on Automated Software Analysis Tasks
  7. 2026Model-Based Agentic Software Engineering
  8. 2026Toward Architecture-Aware Evaluation Metrics for LLM Agents