Raiya reads the incident before you do.
An SRE agent that correlates signals across your stack, proposes root cause with the supporting evidence, and executes only the runbooks you've written and approved.
faster mean time to resolution
Enterprise guardrails, real autonomy.
Your team defines exactly which tools Raiya can use and broadly how it behaves in a Kubernetes custom resource, git backed like the rest of your infrastructure. Every change to what the agent can reach goes through your normal review process before it ships.
For tools that act directly on your cluster, like the Kubernetes or OpenShift MCP, permissions run through the RBAC your platform team already manages, not a separate system to trust. Tools outside the cluster, such as Atlassian or ServiceNow, follow their own permission model instead.
Every run records who triggered it, the execution steps taken, the tools called, and the recommendation or action produced. Runbooks carry their own change log, so you can see exactly what changed, when, and who approved it.
Runbooks: your team's playbook, not the agent's improvisation.
Runbooks go through the same review your team already uses for production changes, before Raiya is permitted to execute them. A given issue gets the same approved response regardless of who's on call.
Automatically in response to a detected incident, on a schedule as a periodic investigation or health check, or manually on demand.
Randoli's own team uses this same workflow to investigate and respond to issues on our own infrastructure.
Full production fidelity. Nothing leaves your environment.
Raiya correlates against your actual logs, traces, and metrics as they're generated, not a downstream copy or a sampled subset. Root cause analysis is only as good as the data behind it.
The agent runs inside your environment, against that same data. Randoli's control plane receives only derived signals and correlation results, never raw logs or traces.
An agent that runs in your environment, not a black box in ours.
Root cause and blast radius, correlated from logs, traces, and metrics.
Register your own custom agents as tools Raiya can call into.
A contextual incident report handed to the on-call engineer, not a blank dashboard.
Already building your own agents? Plug them in.
Raiya doesn't require you to start over. A custom agent your team has already built can be registered as a tool Raiya calls into, using the agent-as-a-tool pattern. It keeps running your own logic; Raiya's CRD-defined permissions and audit trail still govern when and how it's invoked.
How it works
Raiya watches the same OTel signals your team already collects, no separate instrumentation.
It cross-references logs, traces, and metrics to propose root cause with supporting evidence, not just a guess.
Executable runbooks let Raiya take the fix (within permissions your team defines, nothing runs that wasn't pre-approved) or hand the on-call engineer a contextual incident report.
Meet Raiya, in under 90 seconds.
A short animated walkthrough of the idea behind Raiya: detect, correlate, and resolve, without waiting on a human to open a second tab.
Transcript
Modern systems don't fail quietly. They generate noise, alerts, logs, metrics, traces. SREs spend hours just figuring out what's actually wrong before they can even think about fixing it. What if your system could investigate itself? Meet the Randoli SRE agent, Raiya. It continuously watches your systems, detecting issues as they emerge from anomalies, signals, or even human input. The moment an issue is detected, the agent begins its investigation. It correlates telemetry across logs, metrics, and traces, identifying impact, dependencies, and likely root causes in seconds, not hours. For known issues, the agent can take action. It executes pre-approved runbooks safely with guardrails, mitigating problems before they escalate. Every action is documented. The agent creates a detailed issue report with context, root cause analysis, and remediation steps. So, SREs don't start from scratch. They start informed. Now, SREs and agents work together. Humans guide strategy. Agents handle speed and scale. The result: production issues resolved up to 40% faster, with less noise and more clarity. Randoli SRE agent. When systems don't just alert, they investigate.