An automated observation-and-scoring toolkit captures full execution trajectories of LLM agents and performs fine-grained checks to evaluate their instruction following. It disentangles task-solving ability from compliance with scaffold-specified rules, especially under heterogeneous constraints.
This toolkit automatically watches how AI coding assistants perform tasks, recording every step. It then scores them not just on whether they finished the task, but also on how well they followed all the specific rules and instructions, even complex ones. This helps developers understand and improve how well these AIs stick to guidelines.
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