We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational mechanics workflow, from perceptual data of an engineering component through geometry extraction, material inference, discretisation, solver execution, uncertainty quantification, and code-compliant assessment, to an engineering report with actionable recommendations. Agents are formalised as conditioned operators on a shared context space with quality gates that introduce conditional iteration between pipeline layers. We introduce a mathematical framework for extracting engineering information from perceptual data under uncertainty using interval bounds, probability densities, and fuzzy membership functions, and introduce task-dependent conservatism to resolve the ambiguity of what `conservative' means when different limit states are governed by opposing parameter trends. The framework is demonstrated through a finite element analysis pipeline applied to a photograph of a steel L-bracket, producing a 171,504-node tetrahedral mesh, seven analyses across three boundary condition hypotheses, and a code-compliant assessment revealing structural failure with a quantified redesign. All results are presented as generated in the first autonomous iteration without manual correction, reinforcing that a professional engineer must review and sign off on any such analysis.

翻译：我们提出了一种与求解器无关的框架，在该框架中，协调工作的大语言模型智能体自主执行完整的计算力学工作流：从工程构件的感知数据出发，依次进行几何提取、材料推断、离散化、求解器执行、不确定性量化以及符合规范的评估，最终生成包含可操作建议的工程报告。智能体被形式化为共享上下文空间上的条件算子，并配备质量门控机制，在流水线各层之间引入条件迭代。我们引入了一个数学框架，用于在不确定条件下从感知数据中提取工程信息，该框架利用区间界限、概率密度和模糊隶属函数，并引入任务相关的保守性来解析"保守"一词在不同极限状态受相反参数趋势支配时的歧义。该框架通过一个有限元分析流水线进行了演示，该流水线应用于一张钢制L形支座的摄影图像，生成了171,504节点的四面体网格，在三种边界条件假设下进行了七次分析，并通过一项符合规范的评估揭示了结构失效，同时给出了量化的重新设计方案。所有结果均作为首次自主迭代生成，未经人工修正，以此强调专业工程师必须对任何此类分析进行审查并签署确认。