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：我们提出了一种求解器无关的框架，其中协调的大语言模型（LLM）智能体自主执行完整的计算力学工作流程：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、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，能在流水线层之间引入条件迭代。我们提出了一个数学框架，用于在不确定性条件下从感知数据中提取工程信息，该框架采用区间界限、概率密度和模糊隶属函数，并引入了任务相关的保守性，以解决当不同极限状态由相反参数趋势支配时“保守”一词的模糊性。通过一个应用于钢制L形支架照片的有限元分析流水线，我们展示了该框架：生成了171,504个节点的四面体网格，在三种边界条件假设下进行了七次分析，并得出符合规范的评估结果，揭示了结构失效并附有定量化的重新设计方案。所有结果均来自首次自主迭代，未经手动修正。这进一步强调，任何此类分析必须由专业工程师进行审查和签署。