How do I track errors and incidents across my production app?

To track errors and incidents effectively in a production app, you want three layers:

1. Error monitoring — catch exceptions, crashes, failed requests
2. Incident alerting — notify the right people when something is broken
3. Operational observability — understand what happened and how widespread it is

Here’s a practical approach.

1) Use an error tracking tool

Common options:

• Sentry
• Bugsnag
• Rollbar
• Datadog Error Tracking
• New Relic

These tools can capture:

• stack traces
• request context
• user/session info
• release version
• environment
• breadcrumbs leading up to the error

What to capture

Make sure every error event includes:

• app/service name
• environment: prod, staging, etc.
• release/build version
• timestamp
• endpoint/job name
• user or account ID if allowed
• request ID / trace ID
• severity
• tags like region, tenant, feature flag

2) Define what counts as an “incident”

Not every error is an incident. Set rules for escalation, for example:

• error rate > 5% for 5 minutes
• login failures spike
• checkout failures
• queue backlog exceeds threshold
• API latency p95 > X
• critical job failed
• a single high-value customer is impacted

This helps reduce alert noise.

3) Centralize logs, metrics, and traces

Use observability tools so you can correlate problems:

• Logs: detailed events and exceptions
• Metrics: rates, counts, latency, saturation
• Traces: request flow across services

Common stacks:

• OpenTelemetry for instrumentation
• Prometheus + Grafana
• Datadog
• Elastic Stack
• New Relic

4) Add alerting

Send alerts to:

• Slack / Microsoft Teams
• PagerDuty / Opsgenie
• email only for low-severity issues

Good alerting practices:

• alert on symptoms, not every exception
• group related errors
• deduplicate repeated events
• route by service/team/severity
• include links to logs/traces/releases

5) Build an incident workflow

When an incident happens:

1. detect
2. alert
3. triage severity
4. assign owner
5. mitigate
6. root-cause
7. postmortem
8. add a test/monitor to prevent recurrence

Use incident tracking tools like:

• PagerDuty
• Opsgenie
• Jira Service Management
• FireHydrant
• incident.io

6) Correlate errors with deployments

Track:

• deploy time
• release version
• feature flags
• config changes

This helps answer: “Did this start after the latest deploy?”

7) Add dashboards

Create dashboards for:

• error rate
• failed requests by endpoint
• latency
• crash-free sessions
• top exceptions
• incident count and MTTR
• service health

8) Use a consistent taxonomy

Example severity levels:

• SEV1: major outage or data loss
• SEV2: significant degradation
• SEV3: limited impact / workaround exists
• SEV4: minor issue / no urgent action

And categories:

• auth
• payments
• infra
• database
• third-party dependency
• deployment
• frontend
• background jobs

9) Protect user privacy

Be careful not to log:

• passwords
• access tokens
• full credit card numbers
• sensitive personal data

Redact or hash sensitive fields before sending them to monitoring tools.

10) A simple recommended setup

If you want a straightforward, effective setup:

• Sentry for error tracking
• OpenTelemetry for traces
• Datadog/Prometheus+Grafana for metrics
• PagerDuty for incident paging
• Slack for team notifications

If you want, I can also give you:

• a minimal architecture diagram
• a checklist for production readiness
• or example implementation for Node, Python, Java, or React.