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. When the root cause is in your code, Raiya finds it and opens a pull request with the fix.
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.
Even once you know what broke, someone still has to find the offending code, write the fix, and open a PR, often in a codebase they didn't write.
Raiya connects to GitHub, GitLab, or Bitbucket through your Git provider's MCP, traces the incident to the code responsible, and opens a pull request with a proposed fix. It never merges: your normal review decides. Analysis runs in your environment, Randoli has no visibility into your code, and every step is in the audit trail.
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 any tool with an MCP server, such as GitHub, Jira, ServiceNow, or Slack, and register your own custom agents as tools it can call into using the agent-as-a-tool pattern, rather than living inside what shipped out of the box.
A general-purpose AI assistant wired directly to your cluster's tools has whatever access you granted it, with no consistent record of what it's allowed to touch or why, and a different investigation path each time.
Same underlying access, governed by a Kubernetes custom resource and the permissions your platform team already runs, so every engineer gets the same approved path, not a one-off setup.
Most AI incident tools run against a single vendor-hosted model, so your telemetry reaches infrastructure you didn't choose and can't govern.
Raiya's correlation engine runs against your real logs, traces, and metrics inside your environment. When it reasons about an incident, it calls the model provider you configure, AWS, Azure, Google, Anthropic, or OpenAI, under your own account. Randoli's control plane never receives raw telemetry.
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.
See the full capability on SRE Agent (Raiya).