A robust frontend monitoring system answers three questions in production: Is it broken? Is it slow? What were users doing when it happened? That means errors, performance, and behavioral context — collected, enriched, and routed somewhere actionable.

1. Error tracking

• Global handlers: window.onerror / addEventListener('error') for uncaught errors and resource failures; unhandledrejection for promise rejections.
• React error boundaries → report on componentDidCatch with the component stack.
• Manual capture in try/catch around risky event handlers and async code (boundaries don't catch those).
• Source maps uploaded to the backend so minified stacks are readable.
• Deduplicate and group identical errors; track error rate, not just count.

2. Performance (RUM — real user monitoring)

• Core Web Vitals — LCP, INP, CLS via the web-vitals library, reported from real sessions.
• Custom marks/measures (performance.mark), API latency, route-transition timing, long tasks.
• Report p75/p95, segmented by route, device, region — averages hide the pain.

3. Structured logging & breadcrumbs

• Breadcrumbs — a rolling trail of recent actions (clicks, navigations, network calls, state changes) attached to each error so you can see how the user got there.
• Structured logs — JSON with consistent fields, levels (debug/info/warn/error), not free-text console.log.
• Network logging — failed requests, status codes, timing.

4. Context enrichment (what makes reports actionable)

Every event carries: release/version, route/URL, user/session id (or anonymized id), browser/device/OS, feature-flag state, viewport, connection type, timestamp. An error without context is nearly useless; "this error, on release 1.4.2, on the checkout route, for users on Safari" is a bug ticket.

5. Transport, sampling, reliability

• Batch events; send via navigator.sendBeacon (survives page unload) or fetch with keepalive.
• Sample high-volume data (e.g. 100% of errors, X% of performance/replay) to control cost and load.
• Rate-limit so an error loop doesn't DoS your own ingestion or the user's network.
• Buffer offline, flush on reconnect. Monitoring must never break or slow the app.

6. Tooling

Don't build the backend — use Sentry, Datadog RUM, LogRocket, New Relic, Grafana Faro. Add session replay (LogRocket/Sentry) to watch what happened. Wrap the vendor SDK in your own thin module so you can swap it.

7. Alerting & dashboards

• Alert on SLOs: error rate spike, Core Web Vitals regression, a new error type, a crash-rate threshold — routed to Slack/PagerDuty.
• Dashboards for error trends, vitals over releases, top errors.
• Release tracking — tie metrics to deploys so you catch a bad release fast.

8. Privacy

• Scrub PII before sending — mask inputs, redact tokens, anonymize ids in replay.
• Respect consent (GDPR), data residency, and don't capture sensitive fields.

The framing

"Three pillars — errors, performance (RUM), and behavioral context (breadcrumbs/replay) — every event enriched with release/route/user/device, sampled and rate-limited, sent reliably via sendBeacon, routed to a tool like Sentry with alerting on SLOs and release tracking. And PII scrubbed throughout. The monitoring system itself must be lightweight and never able to break the app."