What alerts should AI investigate? A framework for data engineering teams

const metadata = ; One of the most common questions I get from data engineers exploring agentic alert investigation is: "Where do I even start?" It's a fair question. Most teams are drowning in alerts. Between Slack channels lighting up with pipeline failures, Datadog notifications about data quality issues, and PagerDuty pages for infrastructure problems, the volume is overwhelming. The instinct is often to start with the "most critical" alerts - the ones tied to revenue, customer-facing dashboards, or executive reports. But that's not always the right answer. Look at what's already breaking Here's the framework I share with teams getting started with AI-powered alert investigation: Step 1: Pull your last 50-100 alerts Go into Slack, Datadog, PagerDuty or wherever your alerts live and the last 50-100 that fired. The more the better, but even if you just pull 20, at least you'll be working with real data. Now look for patterns. What alerts fire most often? Which ones take the longest to investigate? Which ones pull your team out of deep work repeatedly? Step 2: Map alerts by frequency and effort Create a simple 2x2 matrix: - X-axis: Alert frequency (How often does this type of alert fire?) - Y-axis: Investigation effort (How long does this type of alert take to diagnose?) Now plot your alerts. Step 3: Prioritize based on the quadrant Low frequency High frequency High investigation effort Second priority for automated investigation Top priority for automated investigation Low investigation effort Keep manual or ignore Reconsider alerting setup High frequency + high effort: AI auto-investigate immediately These are your time vampires. They're the alerts that fire multiple times a week and require hours of log diving, metric checking, and hypothesis testing each time. Senior engineers from different teams need to come together to swap context and institutional knowledge. This is exactly where AI investigation shines. AI agents can automatically devise and run tests on multiple root cause hypotheses in parallel the moment the alert fires. By the time your team looks at it, it should be obvious what happened, and how to fix it. High frequency + low effort: Consider auto-fixing or tuning thresholds If an alert fires constantly but only takes 30 seconds to diagnose and dismiss, ask yourself: should this even be an alert? These alerts are candidates for: - Adjusting alert thresholds - Auto-remediation (if the fix is always the same) - Turning into monitoring dashboards instead of active alerts Low frequency + high effort: Second priority for AI auto-investigation These are your 3am production incidents. They're rare but extremely painful when they happen. The ROI of auto-investigation is high here, but because these alerts don't fire that often, it may take some time until you see that return. For that reason, you may decide not to implement AI investigation for these alerts initially, but definitely come back to them when you've tackled the more frequent issues. Low frequency + low effort: Keep manual or ignore These are your "check and dismiss in 30 seconds" alerts. The ones where you glance at a dashboard, confirm everything's fine, and move on. Leave them as-is, or consider whether they should even be alerts at all. Maybe they're better suited as periodic check-ins or dashboard widgets rather than interruptions. Start small, measure impact You don't need to automate everything on day one. Pick 3-5 alerts from that top-right quadrant and set up AI investigation for those. Then measure: - Time saved per investigation - Reduction in mean time to resolution - Engineer satisfaction (are they getting pulled into fewer fire drills?) Then expand from there. If you're interested in agentic alert investigation, why not [try Tracer for free today](https://www.tracer.ai)?