The history of computational materials science is marked by the development of tools that enable researchers to achieve higher levels of abstraction in their manipulation and study of material systems. Standardized engines for first-principles simulations, such as VASP , and molecular dynamics simulations, like LAMMPS , allow researchers to access high-performance algorithms without having to implement them from scratch, see Figure . Libraries such as Pymatgen , ASE , AiiDA , and Atomate 6 streamline routine tasks involving atomic structure manipulation, IO operations for simulation engines, and the extraction of structured data from simulation outputs, and their skillful use can dramatically enhance the productivity of researchers of all skill levels. We believe that even for prosaic use cases, Large Language Models (LLMs) offer a promising route towards further acceleration of routine tasks . In particular, LLMs exceed at tasks that involve endowing unstructured language with structure, which may make them well suited towards the task of generating ordered crystal structures using language. Already, LLMs have been providing robust protocols for extracting data from literature , collecting data via data mining , and applying machine learning to routine tasks. Enhanced by AI agents , workflows that utilize LLMs to convert natural language inputs into precise simulation engine setups could become routine, thereby enabling more intuitive and efficient data manipulation and increasing the productivity of individual researchers at every skill level. Beyond the already-cited tasks like extracting structures from literature, LLMs that are designed for this purpose could serve as an intermediate layer in queries like 'generate all symmetrically inequivalent surface adsorbate configurations of COOH on Pd', as a part of a human-or agent-guided workflow.