# ADR-010: Automated Incident Response and Self-Healing

**Status**: Accepted | **Date**: 2025-01-16 | **Supersedes**: None

## Context

Production incidents require rapid response to minimize impact. Manual responses are slow
and error-prone. Automated incident response reduces MTTR (Mean Time to Recovery).

## Decision

Implement autonomous incident response system that detects issues and automatically
remediates without human intervention.

## Automation Levels

### Level 1: Automatic Detection

```text
Monitoring Alert
    ↓ (triggered)
    ↓
Detection Engine
    ├─ Analyze alert severity
    ├─ Correlate related alerts
    ├─ Assess impact
    └─ Classify incident type
```

### Level 2: Automated Response

```text
Incident Classification
    ↓
Remediation Playbook Selection
    ↓
Automated Mitigation Steps
    ├─ Scale up resources
    ├─ Failover services
    ├─ Restart components
    ├─ Clear caches
    └─ Update routing
```

### Level 3: Escalation and Validation

```text
Auto-remediation Attempted
    ↓
Monitor for Recovery
    ├─ Success → Close incident
    └─ Failure → Escalate to human
    ↓ (human intervention required)
    ↓
On-call Engineer Notified
```

## Implementation

### Incident Detection

```rust
pub struct IncidentDetector {
    alerts: Arc<RwLock<VecDeque<Alert>>>,
    correlation_window: Duration,
    detectors: HashMap<String, Box<dyn IncidentClassifier>>,
}

impl IncidentDetector {
    pub async fn detect(&self, alert: Alert) -> Option<Incident> {
        // Correlate with recent alerts
        let related_alerts = self.correlate_alerts(&alert).await;

        // Classify incident type
        let incident_type = self.classify(&alert, &related_alerts).await?;

        // Assess severity
        let severity = self.assess_severity(&incident_type, &related_alerts).await;

        Some(Incident {
            id: generate_id(),
            incident_type,
            severity,
            timestamp: Utc::now(),
            alerts: vec![alert],
            related_alerts,
        })
    }

    async fn correlate_alerts(&self, alert: &Alert) -> Vec<Alert> {
        let alerts = self.alerts.read().await;
        alerts
            .iter()
            .filter( | a | {
                // Alerts from same service within window
                a.service == alert.service
                    && (Utc::now() - a.timestamp) < self.correlation_window
            })
            .cloned()
            .collect()
    }

    async fn classify(&self, alert: &Alert, related: &[Alert]) -> Option<IncidentType> {
        // Use machine learning to classify incident type
        // Consider: alert patterns, historical data, service dependencies
        Some(IncidentType::HighLatency)
    }
}
```

### Automated Remediation

```rust
pub struct RemediationEngine {
    playbooks: HashMap<IncidentType, RemediationPlaybook>,
}

impl RemediationEngine {
    pub async fn remediate(&self, incident: &Incident) -> RemediationResult {
        // Select appropriate playbook
        let playbook = self.playbooks
            .get(&incident.incident_type)
            .ok_or("No playbook for incident type")?;

        // Execute remediation steps in sequence
        let mut results = Vec::new();

        for step in &playbook.steps {
            match step {
                RemediationStep::ScaleService { service, target_replicas } => {
                    results.push(self.scale_service(service, *target_replicas).await?);
                },
                RemediationStep::FailoverService { service, target_region } => {
                    results.push(self.failover_service(service, target_region).await?);
                },
                RemediationStep::RestartService { service } => {
                    results.push(self.restart_service(service).await?);
                },
                RemediationStep::ClearCache { service } => {
                    results.push(self.clear_cache(service).await?);
                },
            }

            // Check if remediation worked
            tokio::time::sleep(Duration::from_secs(30)).await;
            if self.is_healthy(&incident.incident_type).await {
                return Ok(RemediationResult {
                    success: true,
                    steps_executed: results,
                });
            }
        }

        // If still not healthy, escalate
        Ok(RemediationResult {
            success: false,
            steps_executed: results,
        })
    }
}
```

### Runbook Example: High Latency

```yaml
# runbooks/high-latency-response.yaml
incident_type: high_latency
severity_threshold: 200ms

response:
  immediate_actions:
    - action: scale_up
      service: api-service
      percentage: 50
      wait: 30s

    - action: clear_cache
      service: redis-cluster
      pattern: "session_*"

    - action: drain_connections
      service: load_balancer
      graceful_wait: 60s

  if_not_resolved:
    - action: failover
      service: api-service
      target_region: secondary

    - action: rollback
      version: previous_stable
      service: api-service

  escalation:
    severity: critical
    notify: on-call-engineer
    max_auto_attempts: 3
```

### Nushell Implementation

```nushell
def respond-to-high-latency [] {
    print "Responding to high latency incident..."

    # Step 1: Scale up API service
    let scale_result = (
        provisioning scale \
            --service api-service \
            --target-replicas 10
    )

    print $"Scaled to 10 replicas"
    sleep 30s

    # Step 2: Clear cache
    provisioning cache flush --pattern "session_*"
    print "Cache flushed"

    # Step 3: Check if latency improved
    let latency = (
        provisioning metrics get \
            --metric http_latency_p99 \
            --window 5m
    )

    if $latency < 200 {
        print "✓ Latency recovered to acceptable levels"
        return 0
    }

    # Step 4: Failover if still high
    print "Latency still high, initiating failover..."
    provisioning failover \
        --service api-service \
        --target-region secondary

    sleep 60s

    # Step 5: Verify recovery
    let final_latency = (
        provisioning metrics get \
            --metric http_latency_p99 \
            --window 5m
    )

    if $final_latency < 200 {
        print "✓ Failover successful"
        return 0
    } else {
        print "✗ Auto-remediation failed, escalating"
        return 1
    }
}
```

## Self-Healing Patterns

### Automatic Restart on Crash

```text
Service Crash Detected
    ↓
Health Check Failed 3 Times
    ↓
Automatic Restart Triggered
    ├─ Wait 5 seconds (backoff)
    ├─ Start service
    ├─ Verify startup (30s timeout)
    └─ Health check passes
    ↓
Service Restored
```

### Automatic Config Rollback

```rust
pub async fn handle_config_deployment_failure(
    deployment_id: &str,
    error: &DeploymentError,
) -> Result<()> {
    // If deployment fails due to config error
    if error.is_config_related() {
        log::error!("Config deployment failed: {:?}", error);

        // Automatically rollback to last known-good config
        let previous_config = fetch_last_good_config().await?;
        apply_config(previous_config).await?;

        // Notify team
        notify_team("Config rollback triggered automatically").await?;

        return Ok(());
    }

    Err(Box::new(error.clone()))
}
```

## Escalation Criteria

```nickel
{
  escalation_rules = [
    {
      condition = "remediation_attempts > 3",
      action = "escalate_to_oncall",
      severity = "critical"
    },
    {
      condition = "error_rate > 10% for 5m",
      action = "escalate_to_manager",
      severity = "critical"
    },
    {
      condition = "data_loss_risk",
      action = "escalate_to_cto",
      severity = "critical"
    },
    {
      condition = "remediation_attempts > 1 AND not_in_business_hours",
      action = "escalate_to_senior_oncall",
      severity = "high"
    }
  ]
}
```

## Learning from Incidents

```rust
pub async fn post_incident_analysis(incident: &Incident) {
    // Log incident metrics
    log_incident_metrics(incident).await;

    // Identify improvements
    let improvements = analyze_incident_response(incident).await;

    // Update playbooks based on effectiveness
    for improvement in improvements {
        update_playbook(&improvement).await;
    }

    // Generate post-mortem
    generate_postmortem(incident).await;
}

async fn analyze_incident_response(incident: &Incident) -> Vec<PlaybookImprovement> {
    let mttr = incident.resolution_time;
    let automation_effective = mttr < Duration::from_secs(300);  // < 5 minutes

    if !automation_effective {
        // Escalation or playbook was ineffective
        // Analyze why and suggest improvements
        vec![
            PlaybookImprovement {
                remediation_step: "scale_service".to_string(),
                suggestion: "Increase target replicas from 50% to 100%".to_string(),
            },
        ]
    } else {
        vec![]
    }
}
```

## Monitoring Automation Effectiveness

```bash
# Incident metrics
provisioning metrics incident-automation \
    --metric success_rate \
    --metric mttr \
    --metric escalation_rate \
    --metric false_positive_rate

# Output:
# Automation Success Rate: 87%
# Average MTTR: 4m 23s (Target: <5m)
# Escalation Rate: 13% (Target: <5%)
# False Positive Rate: 2% (Target: <1%)
```

## Safety Mechanisms

1. **Automatic Rollback**: Failed remediations automatically rollback
2. **Circuit Breaker**: Stop retries if remediation repeatedly fails
3. **Escalation Triggers**: Escalate if not resolved in N attempts
4. **Rate Limiting**: Don't repeatedly try same remediation
5. **Blast Radius Limits**: Limit changes to prevent cascading failures

## Consequences

- **Positive**:
  - Reduced MTTR from 30+ minutes to <5 minutes
  - Fewer manual escalations
  - Better system resilience
  - Faster incident response at 3 AM

- **Negative**:
  - Automation can cause unintended side effects
  - Requires comprehensive testing
  - Complex to debug if automation fails
  - False positives possible

## Related ADRs

- [ADR-008: Unified Observability Stack](./adr-008-observability-and-monitoring.md) - Metrics for incident detection
- [ADR-009: SLO and Error Budgets](./adr-009-slo-error-budgets.md) - SLO violations trigger incidents
