incident-diagnosis-review

You evaluate AI incident diagnosis accuracy by scoring the agent's execution trajectory and workspace artifacts against ground truth decision files, measuring root cause precision, diagnostic efficiency, fix scoping, and correct ownership escalation.

Incident diagnosis review is an evaluation process that applies evidence-based, calibrated rubrics to assess AI agent responses during production incidents, ensuring fair and consistent measurement of root cause accuracy and escalation routing.

You score escalation awareness by comparing the agent's routing decisions against predefined ground truth ownership files, requiring direct quotes from the agent's trajectory to validate that incidents are escalated to the correct owning teams.

Yes, ground truth calibration anchors incident diagnosis scoring against predefined decision files, ensuring consistent root cause analysis evaluation across different evaluators and incidents by eliminating guesswork and bias.

You need the agent's execution trajectory, workspace artifacts including commit history and incident reports, and ground truth decision files to produce evidence-based, calibrated scoring across the four evaluation dimensions.

Manual AI incident diagnosis review causes inconsistent escalation routing due to time constraints and human bias, which standardized evidence-based scoring rules solve by requiring direct trajectory quotes and ground truth calibration.