How CheckyWorky works (in 10 minutes)

No. CheckyWorky runs real browser-based journeys through your product — filling in forms, clicking buttons, and asserting that the right things appear. It catches issues that a simple HTTP ping never would.

Yes. CheckyWorky uses a dedicated test account to log in and navigate authenticated flows, just like a real customer would.

Start with every 10–15 minutes for your most critical flows (login, signup, checkout). You can tighten or relax schedules as you learn what matters most.

You get the workflow name, the exact step that failed, a screenshot of the page at failure time, and a direct link to inspect the full run details.

Absolutely. You can run checks against production, staging, or both. Many teams run checks against staging after deploys and production on a schedule.

Most teams can get a first login or checkout journey running in ~10–30 minutes if they have: (1) a dedicated test user (or seed account) with stable permissions, (2) a staging or production URL, (3) a plan for MFA/SSO handling (bypass, test IdP user, or token-based auth), and (4) a Slack channel or email list for alerts. Best practice: start with one high-value journey (login → key page) before instrumenting every flow.

Include: a realistic path that maps to user value (signup, login, search, add-to-cart, create invoice, publish post), stable selectors (data-testid), and one or two assertions (page contains expected text, URL match, API response). Avoid: brittle UI-only checks that depend on animation timing, checking third-party widgets you don’t control, and workflows that mutate production data without cleanup. Best practice is to keep journeys short (3–10 steps), then chain multiple journeys if needed.

They remove the guesswork. Instead of an alert that says “/login is down,” you get: the step that failed (e.g., “Click ‘Continue’”), the error type (timeout, 500, element not found), and a screenshot of the UI at failure. This helps immediately route the incident to the right owner (frontend vs auth vs backend), and it’s especially useful for intermittent UI regressions, expired sessions, or broken redirects.

Common patterns: (1) Use a test tenant/user with MFA disabled or a dedicated “synthetic monitoring” policy in Okta/Azure AD; (2) prefer password-based login for the synthetic user when possible; (3) for magic links, use a test inbox and parse the link; (4) for OAuth, authenticate once and reuse a stored session/token with periodic refresh. Best practice: treat auth as its own journey so failures are easy to diagnose and don’t mask downstream workflow issues.

Use a dedicated synthetic user and tag it everywhere: a distinct email domain alias (e.g., monitoring+cw@), a known user agent, and a unique account/org name. Add safeguards like: suppress outbound emails for that user, disable marketing automation events, and ensure any created objects are auto-cleaned (nightly job) or created in a sandbox workspace. Best practice: create a “Synthetic Monitoring” flag in your app to exclude these events from product analytics.

Use a combination of: (1) retry-once logic for single-step timeouts, (2) multi-location confirmation (only page if it fails in 2+ regions), (3) separate alert routes for warning vs critical journeys, and (4) step-level thresholds (e.g., page load > 8s triggers warning, hard failure triggers critical). Best practice: start with business-critical journeys paging, and route the rest to a non-paging Slack channel.

Uptime checks answer “is the endpoint responding?” APM answers “what’s slow or erroring inside the service?” Synthetic workflow monitoring answers “can a user complete the journey end-to-end right now?” Most teams use all three: uptime for broad availability, APM for root cause, and synthetic journeys to catch UI regressions, auth issues, and third-party breakage before customers do.