And that raise the point about like what benchmark is in this case. Because first of all, you can use it to first release the agent. You can make sure that it works. You can handle edge cases well. You can select the optimal model, and you can debug the traces. That's how you make sure that it works. Next, you can make it right. You can put it as a release gate for your agent and verify that any change to agent stack in didn't reduce regression suddenly. And iterate on hardness. And finally, you can optimize it. You can tune it for better cost or latency. Or you can use the traces to even do error training. Uh later I will say I will share link to our website where we have example how we used simulation environments to fine-tune small plan model to match performance of large plan model for specific tasks. Here effectively, that becomes like trifecta of use cases. Like for you, benchmark becomes part part of agent evaluation, becomes part of integration test for agent for release, and it becomes also training set for agent to improve it.