Design an Intelligent Incident Management System — System Design Interview Practice

Interview prompt

Design an incident-management platform that detects and correlates signals into actionable incidents, maps blast radius, recommends evidence-backed remediation, coordinates responders, and learns from outcomes.

• Define signal identity, incident lifecycle, service ownership/topology, severity, evidence, responders, runbooks, confidence, and audit history.
• Correlate alerts without hiding raw evidence, identify affected services, route escalation, and distinguish suggestions from approved actions.
• Separate detection/routing from enrichment, collaboration, remediation execution, postmortems, and learning; make all transitions idempotent.
• Explain noisy/missing signals, false grouping, privacy, access, replay, observability, and manual fallback.

Requirements and scale assumptions

• Ingest signals, open/update incidents, correlate evidence, calculate severity/blast radius, notify responders, and track acknowledgments/resolution.
• Recommend runbooks and owners, coordinate tasks, record approved actions, generate timelines/postmortems, and learn from operator feedback.
• Support incident merge/split, suppression windows, audit/export, deletion policies, replay, and recovery after notification or enrichment failure.
• Open high-confidence incidents within two minutes while preserving raw signals and clearly labeling uncertain recommendations.
• Handle 1M alerts per hour across 50k services without a single hot key or unbounded synchronous work.
• Do not lose committed state; make retries and duplicate events safe.
• Degrade safely when downstream workers, caches, or external dependencies fail.
• 1M alerts/hour, 50k services, and 100k incidents/day
• Partition by the primary tenant, user, item, or geographic key and isolate hot partitions.
• Keep serving state bounded; retain raw events or durable records for replay and auditing.

• Peak scale: 1M alerts/hour; 50k services — Capacity assumption that drives partitioning and backpressure.
• Latency target: incident open p95 < 2m; evidence retained — User-facing budget for the primary request or read path.
• Durable boundary: Committed before async — Raw alerts, incident records, and approved operator actions are authoritative; correlations and recommendations are derived.
• Async boundary: At-least-once workers — Keep Use clustering algorithms for alert correlation, Implement service dependency graph for blast radius, Use NLP to match incidents with historical resolutions off the synchronous path.

Key entities

• OperationalSignalsignalId, source, serviceId, severity, fingerprint, occurredAt

  Deduplicated signal used by intelligent incident management system grouping and policy.

• IncidentincidentId, serviceIds, severity, status, owner, version

  Versioned incident or operational case.

• ResponsePolicypolicyId, scope, schedule, suppression, escalation, version

  Auditable routing, suppression, or capacity policy.

• DecisionRecorddecisionId, incidentId, actor, evidenceRefs, action, createdAt

  Timeline entry for an escalation, capacity decision, or remediation.

Data flow

1. 1. Ingest and normalize signalsThe intelligent incident management system gateway validates source, service, ownership, fingerprint, severity, and deduplication before publishing a signal.
2. 2. Group with topology and policyCorrelation uses dependency, owner, schedule, and suppression policy to create an explainable incident or capacity case.
3. 3. Commit operational stateThe incident service conditionally commits severity, ownership, acknowledgements, and timeline evidence.
4. 4. Escalate and notify asynchronouslyWorkers schedule responders, reminders, and capacity actions with leases, quiet hours, and deduplicated delivery.
5. 5. Learn and repairAnalytics measures noise, response, SLO, and capacity outcomes while replay workers repair missed events or stale policy.

Deep dives and trade-offs

• Grouping without hiding riskUse topology, fingerprints, time windows, and causal hints to group related intelligent incident management system signals. Keep raw signals and evidence links so operators can split an incorrect group. Never let suppression remove critical severity or prevent an audit trail.
• Escalation and schedulesVersion policies, schedules, quiet hours, and fallback responders; make transitions idempotent. Track acknowledgement and notification receipts separately from incident state. Bound retries and use a dead-letter or manual escalation path for provider failures.
• Noise and capacity qualityMeasure alert-to-incident ratio, duplicate rate, missed critical signals, acknowledgement latency, and action success. Use feedback and post-incident review to tune policies without silently changing historical decisions. Keep capacity forecasts and recommendations distinct from autonomous production changes.
• Suppression versus visibilitySuppress duplicate delivery but preserve raw signals, evidence, and a reason for grouping. Deleting noisy signals makes it impossible to prove that a critical event was missed.
• Synchronous page versus queued deliveryCommit incident state first and queue pages or reminders with delivery receipts. Blocking incident creation on a notification provider turns provider outages into incident loss.
• Automatic remediation versus human controlAutomate bounded, reversible actions and require approval for high-blast-radius changes. An incorrect correlation can make automatic remediation amplify an outage.