Thanks for the link, it taught me a lot.

From what I gather, you can build an agent for a task as long as:

- you trust the decision making of an LLM for the required type of decision to be made; so decisions framed as some kind of evaluation of text feels right.

- and if the penalty for being wrong is acceptable.

Just to go back to the resume screening application, you'd build an agent if:

- you asked the LLM to make an evaluation based on the text content of the resume, any conversation with the applicant, and the declared job requirement.

- you had a high enough volume of resumes where false negatives won't be too painful.

It seems like framing problems as search problems helps model these systems effectively. They're not yet capable of design, i.e, be responsible for coming up with the job requirement itself.

An AI company doing it is the corporate equivalent of "works on my machine".

Can you give us an example of a company not involved in AI research that does it?

There’s plenty of companies using these sorts of agentic systems these days already. In my case, we wrote an LLM that knows how to fetch data from a bunch of sources (logs, analytics, etc) and root causes incidents. Not all sources make sense for all incidents, most queries have crazy high cardinality and the data correlation isn’t always possible. LLMs being pattern matching machines, this allows them to determine what to fetch, then it pattern matches a cause based on other tools it has access (eg runbooks, google searches)

I built incident detection systems in the past, and this was orders of magnitude easier and more generalizable for new kinds of issues. It still gives meaningless/obvious reasoning frequently, but it’s far, far better than the alternatives…

> we wrote an LLM

Excuse me, what?

LLM _automation_. I'm sure you could understand the original comment just fine.

I didn't. This also confused me:

> LLMs being pattern matching machines

LLMs are _not_ pattern matching. I'm not being pedantic. It is really hard and unreliable to approach them with a pattern matching mindset.

if you say so

I stand by it.

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.