Feature Request: Rovo AI should track and report its own health metrics
When Rovo fails to complete an action — like creating a Jira ticket — the only feedback loop is the user noticing, retrying, and eventually giving up or contacting support. There's no mechanism for the AI itself to recognize patterns in its own failures and escalate them.
The problem: If Rovo's ticket creation (or any action) starts failing at a high rate, there's no self-awareness or automatic alerting. Users experience repeated failures individually, with no way to know if it's a systemic issue or something on their end. The burden falls entirely on users to report problems through support channels — which most won't do.
The suggestion: Give Rovo basic operational self-monitoring:
Track success/failure rates per action type (e.g., Jira ticket creation, page updates, searches)
Detect anomalous failure rates and surface them to Atlassian engineering automatically
Optionally, let the AI inform users in-conversation: "I'm seeing elevated failure rates for this action right now — I've flagged it internally"
This is standard observability practice for any production system. The AI layer should have the same operational awareness we'd expect from any service — especially one that users are relying on to get real work done.
Why it matters: As organizations adopt Rovo more deeply, silent failures erode trust quickly. A user who hits three failed actions in a row with no explanation will stop using the tool. Self-monitoring would catch systemic issues faster, reduce support burden, and make Rovo a more reliable teammate.
