AI Agent for Production Incident Investigation & Recovery
Autonomously investigate production incidents across multiple data sources and orchestrate recovery.
Problem Context
Production incidents cost organisations millions in downtime and engineering hours. Traditional runbooks are manual, slow and rely on institutional knowledge. An AI agent that can span monitoring dashboards, logs, traces and alert histories to pinpoint root causes — and safely act on them — transforms incident response.
Challenge Requirement
Build an AI agent that autonomously investigates a production incident from initial alert through to a structured post-incident report, with human approval gates for high-impact remediation actions.
Mandatory Capabilities (8)
Illustrative Scenario
A spike in payment failures triggers a PagerDuty alert. The agent correlates Datadog metrics, Elasticsearch logs and a recent deployment event, hypothesises a database connection-pool exhaustion caused by the new release, presents a confidence-annotated root-cause summary to the on-call engineer, and — upon approval — rolls back the deployment and restarts the affected services.
Safety & Governance Rules
All high-impact remediation actions (restarts, rollbacks, configuration changes) must require explicit human approval before execution. The agent must record a complete audit trail.
RECOMMENDED EXECUTION TRACE
BUILD REQUIREMENT
Demonstrate an end-to-end working system that ingests a realistic incident scenario, reasons across multiple data sources, proposes a root cause with confidence estimate, plans remediation steps, requests human approval, executes approved actions, and generates a structured post-incident report.