Terminal Agents: A Survey of AI Agents in Command-Line Environments

LLM-based agents increasingly interact with external environments through terminal command execution, yet existing surveys have rarely treated the terminal itself as a primary analytical object. This survey examines terminal agents, namely systems whose task progress depends on iterative command execution, textual feedback, and stateful terminal command interaction, and clarifies their boundaries with adjacent categories such as software-engineering agents, GUI- or browser-based computer-use agents, and CLI-packaged assistants. Through a substrate-centered lens, we systematize the literature around architectures and outer-loop design patterns, competence acquisition through executable environments, command--observation trajectories, and post-training, and evaluation protocols for terminal-mediated capabilities. Across systems, acquisition pipelines, and benchmarks, the synthesis shows that outer-loop design is not an implementation detail but a first-class variable that materially shapes measured performance. The evidence further indicates that terminal competence is multi-dimensional, spanning how agents formulate actions, interpret feedback, manage runtimes, track state and context, verify progress, recover from failures, and control side effects, rather than reducible to a single capability ranking. Current evidence remains concentrated in software engineering, while cross-domain transfer, model-versus-scaffold attribution, reliable recovery in mutable environments, and process-level evaluation remain underdeveloped. The survey provides an evidence-calibrated map of established findings, emerging practices, and unresolved challenges for terminal agents.