10x = AI Log Infra

Traditional observability pipelines and log analyzers were designed to let SREs operate applications coded by developers. Now the same frontier models powering coding agents power the SRE agents reading the semi-structured logs they emit.

Cost explodes on two axes: volume the analyzer ingests from code written by agents, and tokens AI SREs burn to read those logs back. The problem is structural: log data was never meant to be consumed by AI models.

10x bridges the gap between coding agents and AI SREs. Logs become the structured context and time-series metrics models need to diagnose issues and cut cost.

The 10x Engine executes in compile and run-time modes, similar to V8 for JavaScript or the JVM:

The polyglot 10x Compiler statically analyzes Helm charts, container images, and GitHub repositories to build a vocabulary of the log structures an environment's applications and infrastructure emit at run time.

The 10x Runtime applies that vocabulary to create distilled context AI SREs can reason on without burning tokens or probabilistic sampling. Every event is assigned a template and time-series metric, the way every object shares a class in V8 or the JVM.

AI Models leverage the 10x context plane to correlate operational incidents and logging costs with the exact code and config changes that caused them. SRE Agents direct the Runtime to losslessly compact noise, or offload to S3, dropping cost and investigation time 50–80% in benchmarks.