1 question to an AI agent: 24 spans, 10 model calls, 160K input tokens.
Elastic 9.5 records all of it out of the box. There's no collector to set up.
Every Agent Builder run lands as OTel traces in your own cluster, down to each ES|QL query the agent wrote and which index it
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
