An on-call engineer who's already read the incident.
Correlating a log line, a trace, and a metric spike by hand while production is down is slow, and most AI tooling asks you to trust a black box with production access. Raiya runs in your own environment, with full data sovereignty, and works inside the guardrails your team sets, not around them.
Lining up a log line, a trace, and a metric spike by hand is the slowest part of an incident, and it happens under the most pressure.
Raiya correlates signals across logs, traces, and metrics and proposes a root cause with the supporting evidence already gathered.
Handing an autonomous agent open access to production trades one problem for a larger one.
Raiya only executes runbooks your team has written and approved, with permissions your team defines in advance per action.
An assistant that only reaches its own vendor's integrations stops being useful at the edge of that vendor's product.
Raiya is an extensible framework: plug in third-party MCPs, custom agents, and your own tools rather than living inside what shipped out of the box.
Debugging an incident shouldn't mean exporting your production telemetry to somebody else's model.
Raiya runs against signals processed locally in your environment, and the control plane never receives raw logs or traces.
See the full capability on SRE Agent (Raiya).