Full traceability from feature request to production.
Engineering organizations need to understand what AI changed, why it changed, and what effect it had in production. The Observability Layer provides complete end-to-end traceability.
Nothing is a black box.
When an incident occurs at 3am, you can trace it back to the exact agent action, the context it used, the policy that approved it, and the human who reviewed it — in seconds, not hours.
End-to-End Workflow Tracing
Every change is traced from the initial signal (ticket, alert, request) through every agent action, human review, deployment, and into production behavior. One trace ID across the entire lifecycle.
Agent Decision Logging
See exactly what context each agent consumed, what alternatives it considered, and why it chose a particular approach. Full explainability for every AI decision.
Production Correlation
When an SLI degrades or an incident triggers, trace back to the exact workflow, agent action, and policy decision that approved the change. Designed to reduce mean time to root cause from hours to seconds.
Replay & Diff Analysis
Re-run any workflow with different context or policies. Compare execution paths side-by-side. Understand 'what would have happened if' for post-incident analysis.
How tracing works
Instrument
Every workflow step, agent action, and system event is automatically instrumented. No manual setup required for LoomStack-orchestrated workflows.
Trace
Events are correlated into end-to-end traces with a single workflow ID. Spans nest from feature request through to production.
Correlate
Production metrics (SLIs, error rates, latencies) are linked back to the specific deployments and changes that caused them.
Alert
Anomalies trigger automated investigation — tracing the impact back to a specific agent decision for immediate root-cause attribution.
Integrates with your existing observability stack
Designed for attribution from incident alert to root cause. End-to-end traces correlate production signals to the workflow, agent action, and policy decision behind the change — not a manual investigation thread.
Make AI-driven engineering fully observable.
Every agent action, every policy decision, every production impact — traced and correlated.