Adapting execution harnesses to individual task instances—rather than using a single global harness—consistently improves agent performance, and this adaptation can be automated by learning from previous optimization runs.
This paper introduces Turbo Harness, a system that automatically customizes AI agent execution frameworks (harnesses) for individual tasks by learning from past optimization runs. Instead of using one fixed harness for all tasks, it generates task-specific modifications that improve agent performance across diverse domains like interactive tasks, coding, and long-horizon planning.