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That works for well trod paths, e.g “fix ci” works exceedingly well. “why app slow” obviously doesn’t work because the task is underspecified. But in order to properly specify you either need an experienced engineer who knows how to narrow the problem domain, or you have to provide some template instructions/output formats (e.g, skills) which will invariably never fit the problem perfectly
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> . “why app slow” obviously doesn’t work because the task is underspecified.

Not always. In my case LLM goes to grafana mcp, pulls metrics/traces/cpu profiles. Figures out what is slow and proposes a solution.


In my cases it always used linux perf to sample the calls, because that's the best tool for my jobs. Never had to tell it to use instrumentation.

I wouldn't agree. Sota models can do self-directed sampling, profiling, benchmarking, read call trees, etc. to give you a report of the app's bottlenecks and then recommend solutions that can be vetted.

I do this constantly.

As the upstream comment points you, you don't need to specify. Sota models are that good. And by being overprescriptive you can accidentally shut off branches that they would've taken, downgrading the quality of their work.


In my experience if you’re at the point where you have something to sample then the hard part is already done.

In a perfect world everything is covered by distributed tracing and the problems are only in your application code and the agent just needs to find the data

In reality the data is often missing or misleading. “Your observability sucks”? Yeah, but that’s life


> “Your observability sucks”? Yeah, but that’s life

You could start by asking your AI "help me add better observability to our stack"


Believe it or not, you can’t just run a profiler on everyone’s browser

I use skills. The skills are not typically "how to perform a task in detail" they are more about what relevant tools and knowledge are required to work in a domain. That is I give the LLM the information it needs about the system but not a sequence of how to accomplish a task. I treat it more like a human and less like a computer.

It really doesn’t need to be that much more specified, give it context to the tools and level of analysis you expect then “why app slow” is a reasonable prompt

> “why app slow” obviously doesn’t work because the task is underspecified

Definitely not true and like everyone else is saying, shows how people still underestimate these models.

I have been working on a simple vite + react app lately and commonly ask Gemini/Antigravity to just "improve speeds", "x is running slow, check it out" and have no complaints.


I’m not surprised it works on a simple app.

disturbingly, when I was using antigravity with gemini pro it was actually quite good at working out 'why app slow' types of problems. Maybe I've been lucky but it seems really good at determining why something might be wrong. It may ask for more logging or diagnostics and run for a long time but it was really digging in and making changes or suggestions to solve the problems.

Honestly I have had great success with “I’m worried here about cpu and latency, please rigorously profile and propose fixes”.

The models can build micro-benchmarks with a level of rigor that few could muster for a new feature.

I agree that if the issue is architectural they will struggle to understand that scope.




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