Voice Incident Copilot
A voice-first production incident copilot designed around a strict separation between observations, hypotheses, runbook knowledge, read-only evidence tools, and consequential actions.
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Latency rises eleven minutes after a deploy.
The easy mistake is to turn correlation into a root-cause claim. This demo forces the agent to collect evidence first.
Alertapi-gateway p95 latency = 2.8s; deploy rc3 = 11m ago.
Voice“I see a latency regression after the latest deploy. I’m checking service health and the rollback runbook.”
KnowledgeRetrieve rollback decision criteria from the incident runbook.
Toolget_service_health(api-gateway) — read-only.
Result8/8 replicas healthy; error rate elevated; downstream dependency nominal.
Voice“The deploy is the strongest current suspect. The runbook says rollback is appropriate if the threshold persists. Want me to prepare the rollback request?”
Keep the voice layer useful by keeping the boundaries explicit.
Conversation
ElevenLabs handles listening, turn-taking, and spoken responses. Runtime incident context enters as data, not a giant static prompt.
Knowledge + evidence
A compact runbook grounds procedure. Health and deploy checks are read-only tools that update the hypothesis before action.
Consequential actions
Rollback, scale, acknowledgement, and similar mutations stay behind an explicit human authorization boundary.
A practical technical content pilot, not a reach guarantee.
The full pilot package includes a 75-second video script/shot list, LinkedIn-native post copy, the agent system prompt, a sample runbook, tool contracts, and a web-demo implementation pattern with a private-agent signed-URL option.
The evaluation target is semantic: did the agent separate evidence from inference, consult the runbook, and request authorization before a consequential action?