Yeah something about this whole concept is way, way off. The energy, and therefore cost, of an LLM analyzing every data point of every time series is many orders of magnitude too high, unless your operations are trivial.
I don't think the idea is to give agents raw data, but for it to run many queries. Humans also don't look at terabytes of raw logs.
I encounter this problem a lot in my work. Agents being run on a cron for instance, where the task is not dynamic and a simple script can be more than sufficient. As agentic use increases i think it will get worse, as scripting is hard to do elegantly, even if the agent is writing the script.
But having an agentic call for every single detail is just so inefficient.
I think there running both will be winning combination scripts/classical ML do large scale crunching and when they find some anomaly or some threshold creates alert. Agents will create initial report and maybe create first pr for developer.