the LM inference using responses. But, what happens in the actual harness? The first step, arguably one of the most important ones, is context construction. And during that, we care about three things quite a lot. The first one is size. We want to make sure that, you know, we don't blast through your token budgets and throw in a bunch of unnecessary content, but also the more context you have in your in your context, the higher it is that you have contradicting information and it causes confusion for the model. The other part is flexibility. We want to make sure that, regardless of how many or how little skills you're using, you have a great experience regardless of how many plugins and MCPs you install. And of course, we want to make sure that things are performing and cost we know you're cost sensitive, so cashability is important as well.