While interest in this relationship is growing, relevant work remains scattered across communities, siloing insights and narrowing the scope of inquiry. This exploratory research agenda seeks to address that fragmentation by treating an agent’s working environment as a distinct object of study, drawing together perspectives from otherwise separate disciplines to better define what constitutes an agent’s workspace and how differences across workspaces constrain or enable the work an agent can perform.

This agentic shift has substantially expanded what a given language model can reliably accomplish, allowing them to better reason through a task, take actions, evaluate results, and modify their work as needed. Coding agents such as Claude Code and OpenAI’s Codex have been particularly successful examples of this development. Yet while all software is written in code—making it tempting to assume agents could be harnessed and transferred to to similarly succeed at other jobs that use computers—not all digital workspaces are built or bundled the same. What we can expect to find in a programmer's working environment (e.g. executable artifacts, version histories, dependency structures, error messages, tests, and reversible actions) may not exist in comparable forms across occupations. If the ability of coding agents, and LLM agents more generally, to successfully complete the work given to them depends not only on their underlying model's trained capabilities but also on how a given workspace allows those capabilities to be reliably enacted then a model-centric understanding of AI is necessarily incomplete. What an agent can actually accomplish must instead be understood relationally, as an outcome of the interaction among the model, the work it is asked to perform, and the workspace through which that work becomes perceptible, actionable, and verifiable.