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Pretty wild. I can see these kinds of ai-based tools become used more often as they connect to more systems.

With that said, in my experience, usually, coming up with a good AI question can sometimes be harder than the act of looking for the data itself, i.e. asking "what were the last 3 errors yesterday" is not something I have ever done.

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Those are good points, but like you said, the more systems connected the deeper the value the tool can bring. It goes beyond a better interface for querying logs and metrics: since our agent can run code, it can give you the bottom line for questions you have based on information aggregated from multiple data sources. You don't need to think of "AI question", rather just ask directly the thing you want to know, and our agent will query multiple sources of data and analyze those to give direct answers. For example, if you're trying to understand who are the users impacted by a certain issue - you can just ask exactly that.

True, there is so much to say for and against AI-based interfaces for working with data. But in reality, following the advent of products such as code interpreter, we're actually seeing users beginning to expect their data to be accessible not just via baroque query languages and multi-hop UXs. Finally, as Yasmin noted above, "Last 3 errors yesterday" is a bit diminutive :) you might ask where are transactions dropped, correlate errors with metrics, create dashboards on the fly, ...

Please feel very free to continue this discussion here -- it's an important one for us!

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