You can definitely take a base LLM model then train it on existing, prepared root case analysis data. But that's very hard, expensive, and might not work, leaving the model brittle. Also, that's not what an "AI Agent" is.
You could also make a workflow that prepares the data, feeds it into a regular model, then asks prepared questions about that data. That's inference, not pattern matching. There's no way an LLM will be able to identify the root cause reliably. You'll probably need a human to evaluate the output at some point.
What you mentioned doesn't look like either one of these.
You can definitely take a base LLM model then train it on existing, prepared root case analysis data. But that's very hard, expensive, and might not work, leaving the model brittle. Also, that's not what an "AI Agent" is.
You could also make a workflow that prepares the data, feeds it into a regular model, then asks prepared questions about that data. That's inference, not pattern matching. There's no way an LLM will be able to identify the root cause reliably. You'll probably need a human to evaluate the output at some point.
What you mentioned doesn't look like either one of these.