AI automation
Otto Monitor: the AI that watches the AI
Our production voice agents are monitored around the clock by an AI ops layer that investigates every alert and posts a root-caused incident report before we have even seen it.
The problem
Once an AI agent answers a client's phone line, 'it seems fine' is not an operating standard. Something eventually goes sideways, and finding out from the client is the worst way.
Raw alerts are noisy too. A failed call might be a real defect, or just someone testing the line and hanging up.
What we built
Seven alert rules watch the agents day and night and feed an n8n catcher. Each alert wakes Otto, which pulls the actual calls behind it, QAs them to separate real issues from test noise, root-causes what is real, posts an incident report to Slack, and acknowledges the alert.
The loop is deliberately conservative: it can read everything and touch nothing critical. Any infrastructure action sits behind an approval gate and an append-only log.
The result
Incidents arrive as reports, not surprises
24/7
coverage on live agents
7
alert rules in production
6-step
automatic analysis loop