I agree with your statement and explained in a few other comments how we're doing this.
tldr:
- Something happens that needs investigating
- Main (Opus) agent makes focused plan and spawns sub agents (Haiku)
- They use ClickHouse queries to grab only relevant pieces of logs and return summaries/patterns
This is what you would do manually: you're not going to read through 10 TB of logs when something happens; you make a plan, open a few tabs and start doing narrow, focused searches.
In this piece though--and maybe I need to read it again--I was under the impression that the LLM's "interface" to the logs data is queries against clickhouse. So long as the queries return sensibly limited results, and it doesn't go wild with the queries, that could address both concerns?
I'm guessing that intention was to say "around 10 lines", though it kind of stretches the definition if we're being picky.
O(some constant) -- "nearby" that constant (maybe "order of magnitude" or whatever is contextually convenient)
O(some parameter) -- denotes the asymptotic behavior of some parametrized process
O(some variable representing a small number) -- denotes the negligible part of something that you're deciding you don't have to care about--error terms with exponent larger than 2 for example