This isn't anything new. It's not particularly technical or novel in any way, but it seems to work pretty well for identifying anomalies and comparing series over time horizons. It's even less token efficient on small windows than piping in a bunch of json, but it seems to be more effective from an analysis point of view.
The strange thing about it is that it involves fairly deterministic analysis before we even send the data to the LLM, so one might ask, what's the point if you're already doing analysis? The answer is that LLMs can actually find interesting patterns across a lot of well presented data, and they can pick up on patterns in a way that feels like they are cross-referencing many different time series and correlate signals in interesting ways. That's where the general purpose LLMs are helpful in my experience.
Breaking out analysis into sub-agents is a logical next step, we just haven't gotten there yet.
And yeah the goal is to approximate those of us engineers who are good at RCAs in the moment, who have instincts about the system and can juggle a bunch of tabs and cross reference the signals in them.
Have an array of scripts to run against each log (just rust code probably for speed) and have them flag for performance, errors, intrusions, etc...