Checkly

Checkly now has AI wired into every stage of the monitoring lifecycle: agents that write checks, Rocky AI for root cause analysis, and incident resolution from the terminal. On the 28, Stefan Judis will connect all AI capabilities into a single live workflow, from generating checks with a coding agent to closing an incident without ever opening a dashboard. Register → https://hubs.ly/Q04cvVTy0

Control who sees your status pages! 🔒 Checkly status pages now support password protection, so you can share service health with the right audience without exposing it publicly. Use it for internal teams, partners, or select customers. Turn it on, share the password, and keep access limited to the people who need it. No SSO setup required. Learn more -> https://hubs.ly/Q04cjS4n0

50% of Checkly CLI users are already coding agents. In our latest session, Stefan Judis showed what that means in practice. He ran two live demos: first, setting up a full monitoring suite (Playwright checks, URL monitors, API checks) in a single agent conversation. Then, triggering a real production failure and letting the agent detect it, trace the root cause in git, open an incident, deploy the fix, and resolve it. All through the CLI. Link in comments.

Our last Playwright webinar generated a lot of follow-up questions about one topic: how to manage environments across local, CI, and production without maintaining three separate configs. So we wrote a guide that covers exactly that: https://hubs.ly/Q04bTWxr0

Synthetic monitoring in production is hard, especially on Shopify. Cookie banners, bot protection, and checkout flows can all get in the way of testing what matters most. We’ve just published a guest post from Vince Graics at World of Books on how his team made Shopify monitoring work in practice using Playwright and Checkly. Thanks to Vince Graics for collaborating with us on this one! Read the full article here: https://lnkd.in/dFyg653M #Playwright #Shopify #SyntheticMonitoring #MonitoringAsCode

"Why would you run Playwright against production?" Stefan's answer: "Why not?" In the session, he took the same test files running against localhost and deployed them as scheduled production monitors with two commands: npx checkly test — runs your Playwright suite from any global location npx checkly deploy — turns those tests into monitors that run every 10 minutes Same code. No rewrites. If your login breaks in production at 3 AM, you know before your users do. Link in comments.

On Tuesday: Stefan Judis demos a full incident lifecycle handled by an agent through the Checkly CLI. No dashboards, no context switching — one agent conversation. Save your spot → https://hubs.ly/Q049_2kF0

When your website generates hundreds of millions a year, you can't afford blind spots. Use synthetic monitoring to catch errors before your users do. That's how Puma keeps running around the clock. 👉 https://hubs.ly/Q04942qn0

Stefan ran a live demo of Claude with Playwright MCP during the session. One sentence prompt: "Navigate to the site, click the first product, add it to cart." The agent browsed the page using the accessibility tree, wrote a test file, ran it, and when it failed, it fixed its own locators and re-ran until it passed. Impressive, but Stefan's takeaway was clear: AI scaffolds tests fast, but you still need to review what it writes before it goes into your suite. The full demo is in the recording. Link in comments.