Terminal Agents

Terminal agents use a shell, filesystem, and command-line tools to inspect and change digital environments, often across long workflows that require planning, debugging, and recovery. Common training approaches include supervised fine-tuning on task trajectories, reinforcement learning with executable verifiers, and synthetic generation of tasks and environments; some methods also teach agents to predict how environments respond to actions. Benchmarks such as Terminal-Bench measure task completion, while newer evaluations test real-world workflows, multimedia tasks, and whether agents follow relevant instructions without being misled by distractors. A central challenge is making results reliable and transferable, since scores can depend on the harness and runtime, and task success alone may not capture process quality or safe, task-aligned behavior.