Trace analytics
Turn recurring corrections into better guidance.
Analyze incoming and historical repository-linked traces. Surface recurring corrections and conventions, then propose improvements to shared skills and repository instructions.
LatentStack is a modular AI software stack. This component is available on its own.
How it works
01
Patterns across agent traces
02
Suggestions for skills and AGENTS.md
03
Human review of guidance updates
Illustrative trace analysis · no model-weight training
Works with
Sources
Repository-linked traces from LatentCode and from configured managed agents.
Targets
Proposed updates to SkillPatch skills and to repository instruction files, including AGENTS.md.
Review
Approval follows the team structure configured in LatentGateway.
Supported-capability list v0.1 · owned by engineering · requires engineering sign-off before publication.
What it does not do
−It does not train model weights. It proposes guidance updates for human review.
−It does not apply changes automatically.
−It has no published evaluation. No accuracy or improvement number is claimed.
Deployment note
Traces are platform content and follow the deployment mode you choose. Under on-premises and air-gapped configurations they stay inside your environment.
Works alone. Better with
Questions
Do you train on our code?
No training on Customer Content.
What happens to a pattern once it is found?
It becomes a proposed update to a shared skill or a repository instruction file. A person reviews it before it applies.