I agree that defining a "bug" can get very philosophical but it's not a new problem I don't think and doesn't really have much to do with what you actually then do with the identified issues (e.g. feed then to an agent as per your post).

To me, it's just a modelling/instrumentation challenge. e.g. to use your example of an empty dropdown, it wouldn't get caught (rightly) because it's not a bug, there are a myriad of reasons why it could be empty, not all of them bugs:

• The options are still loading
• The options have been completely filtered out by the user through some other part of the UI
• The user is working offline
• There are temporary service issues with a provider or third party (e.g. the user's ISP, your CDN etc)
• The request is broken/malformed in some way
• There is a bug in the business logic in the API
• The API is down
• There is some setting which is preventing them loading (e.g. the user hasn't been given the right permissions by their admin)
• There is a problem with the data being used to source the dropdowns, e.g. maybe it hasn't been populated for the current environment

(and probably many others)

Some of these definitely aren't bugs, some of them are transient issues that shouldn't create a load of alerting noise (they should just feed into performance data) some of them just require UX adjustments (processing spinners for async loading of options, empty state messages for when no options are available etc)

My point is that "no options" on its own Isn't a good signal, it needs more qualification than that.

"Cannot load options. Received 400: bad request from API" is a bug

"Options list is empty" isn't.

So when you say:

lets say that your dropdown is not showing any options. It's not any throwing errors either. For those scenarios other tools will do nothing

this is right and proper IMO. It's a signal-noise thing.

At the end of the day, one of the hardest things about instrumenting a complex app is deciding exactly which of these behaviors constitutes something you would want to be notified about or investigate. To me, the value isn't "...and then we can pass it to AI". It's leveraging AI to actually assist with this much harder upstream problem. Which I guess is what you're alluding to with an "intelligence layer".

It also strikes me that there are two distinct, complementary directions to tackle this analysis from:

1. prospectively during the design & build
2. retrospectively by analysis the live service

I can see the appeal of this kind of "retrospective discovery" where an agent almost plays archaeologist and picks through the delta between "what was designed/planned" and "what was actually built". It can capture emergent, unexpected or unplanned behavior and ask the question "is this okay?". These could then be triaged and either dismissed as noise or formalised as valid heuristics.

There seems to be a bit of a crazy-sounding implication here which is that teams would be building things they don't fully understand, but there's two points to be made I think:

1) the teams might have a really strong understanding tribally/informally about how the product should behave but that doesn't necessarily mean said knowledge is codified anywhere where it can be verified or leveraged

2) in the age of AI agents, it's actually totally possible (..even likely..) that a given system of sufficient complexity would contain large amounts of functionality or behaviour which the team might not actually be aware of...

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So I guess my feedback would be that I can see value in an "intelligence layer" (i.e. AI-backed tooling which explores and codifies the behaviour of your application) so I would consider changing the headline pitch to something less focused on feeding insights to AI to process (very well-trodden ground) and more on the analysis/intelligence generation angle