An AI operations assistant that brings service signals and approved playbooks together, with people in control of production changes.
Discuss this approach
During an incident, responders jump between alerts, logs, service history and documentation. Important evidence is easy to miss, handoffs take time and a general-purpose AI assistant cannot reliably determine which changes are safe for production.
Bring relevant service metrics, traces, recent releases and owned playbooks into a scoped investigation view. Use access-controlled connectors, including MCP where appropriate, to retrieve the information the responder is permitted to see.
Have the assistant assemble an incident summary, cite its evidence and suggest next checks. Show uncertainty and missing information so the responder can judge the recommendation.
Separate read-only investigation from execution. Require explicit approval, scoped permissions and an audit trail for production actions, with recovery steps agreed in advance. Test the assistant against past incident scenarios before a live pilot.
Connect delivery decisions to growth, operating efficiency and customer trust.
Help responders find relevant evidence and owners sooner.
Keep the proposed action, supporting evidence and approval together.
Turn reviewed incident findings into better playbooks and service improvements.
Track progress against a shared baseline.
Compare time to a useful diagnosis with a historical baseline.
Review evidence accuracy, unsupported claims and responder acceptance.
Track approval coverage, inappropriate action attempts and audit completeness.
Evaluate previous incidents using approved, sanitized examples.
Pilot read-only investigations alongside the on-call team.
Add tightly scoped actions only after quality and approval controls are validated.
Selected around the workload, existing systems and agreed operating responsibilities.
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