So, let's just let's bring a picture into the mix because it tends to make things more interesting. Uh here's an example we've built of an SCD consensus cluster that a typical production service might rely on. So, an old environment uh might tended to operate and work primarily on that little blue square entitled SCD source code in the bottom right there. But, a lot of the fun and the model capability gap that results from it is really in everything that surrounds it. So, you you start with the tickets, projects, postmortems. What are the train wrecks? Why did they happen? How did customers feel about them? And often times those aren't necessarily up to date. Um the agent has to incorporate all that when it's reasoning through the actual change that current environments have it make. After it makes that change, uh you need a kick kick off rolling deployments. Those deployment systems can often times be complicated, have conflicts, may not work. Um and all through that, when you're finally migrating off of from old hard onto new hardware, um you run into unforeseen problems which you did not sort of the the the the the the agent has to reason through in real time, just like a human would, right? You have um failing nodes. You have stale deprecated nodes. And while all of this is happening, the service can't go down because there is a blast radius to serving live traffic. You have to observe and monitor your service. All of these components in in in the system is really uh what sort of exemplifies like a full end-to-end infrastructure task. >> So, what Sid is describing here is an