> . “why app slow” obviously doesn’t work because the task is underspecified.
replyNot always. In my case LLM goes to grafana mcp, pulls metrics/traces/cpu profiles. Figures out what is slow and proposes a solution.
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.
replyI 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.
replyIn 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
replyYou could start by asking your AI "help me add better observability to our stack"
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.
replyIt 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
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