So I'm wondering, what was your process for figuring out what are the initial set of capabilities that we're going to target and focus on and refine, particularly given that agentic capabilities are still being discovered as far as how best to architect and implement them, and also there is the probabilistic element, so you want to make sure that whatever you're doing, it has a high success rate and a low error rate, particularly for the target audience that you're focusing on?

I guess it speaks about, like, the journey that we've actually taken with Auto.

So last year we released a product called the Astro IDE, which is an in-browser, web-based interface for authoring DAGs or modifying them, and we also have a workflow for testing DAGs against, like, ephemeral deployments so that you can easily spin up the DAG, quickly test it, and then obviously commit your, commit the changes so that you can push them downstream and do whatever you need to there.

What we started to see with this interface that we had for the Astro IDE was the type of questions that people were obviously going to be asking as they interacted it, with it.

Obviously, the, the first use cases that Auto in, in the end solved for initially was the DAG authoring and DAG modification use cases.

But we also saw as well that a lot of folks would be using it for troubleshooting, so the case for building out specific capabilities for investigations became the, the next big angle that we looked at.

We also did see as well that a lot of folks would also use it for upgrading their code at the many different levels and helping using the agent to make those code changes, especially when there's breaking changes involved with actually changing the structure of your code.

I don't even have to think about it." Just some of the, I guess, misconceptions or proper approaches for how to introduce it to a team, especially if they haven't already built up that muscle of working heavily with agents for other areas of their engineering work.