canary

Post-deploy canary monitoring detects performance regressions in live apps by taking periodic screenshots and comparing them against visual baselines. It automatically alerts on anomalies to enable quicker discovery and faster recovery.

Baseline-based anomaly detection captures periodic screenshots of your running app and alerts you when visual or functional deviations occur. It compares live application states against captured baselines to proactively catch production errors.

You can set up post-deploy monitoring by invoking the canary skill immediately after deployment. It uses a browse daemon to begin continuous monitoring, capturing screenshots, detecting console errors, and alerting on page failures.

Yes, canary monitoring applies to production deployments across both web and mobile apps. It validates new deployments and monitors critical routes by capturing visual baselines and alerting on detected anomalies.

Real-time monitoring prevents customers from noticing errors and performance regressions by catching issues immediately after deployment. It watches the running app and raises alerts when production anomalies appear against established baselines.

Screenshot-based production anomaly detection relies on visual baseline comparisons to catch regressions. While it effectively identifies visual deviations and page failures, it requires a running browse daemon to continuously capture and compare application states.