Answering from precomputed facts cut agent token use by 93%.
Standard agentic RAG retrieves whole documents and dumps them into context on every question. The cost repeats with every miss.
We precomputed the facts instead. A Kibana Workflow distills each document into a
Your ES|QL query dies with Unknown column [field] because an alias got repointed and the new backing index dropped a field.
Or you spot a field in your documents that never made it into the mapping. Until now, using it meant a reindex. Hours of it.
In Elasticsearch 9.5, SET
470 of the 500 slowest requests share 1 log pattern.
You'd never find it reading traces one at a time.
At 10 traces the check is tedious. At 500 it doesn't happen.
ES|QL subqueries are now on Serverless and in tech preview in 9.5
WHERE trace_id IN (subquery)
The trace query
Elasticsearch 9.5 cuts timestamp storage by 92%.
1.03 GB down to 79 MB on a high-cardinality benchmark of 2.26 billion data points.
The old time series codec applied one fixed encoding to every numeric field. Fine for timestamps and counters. Useless for floats: a 0.01 change
