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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 so
by herval 1y ago
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…
- alganet 1y ago> we wrote an LLM Excuse me, what?
- herval 1y agoLLM _automation_. I'm sure you could understand the original comment just fine.
- alganet 1y agoI 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.
- herval 1y agoif you say so
- alganet 1y agoI 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.