Operations | Monitoring | ITSM | DevOps | Cloud

Most agent skills are static — frozen documentation snapshots that go stale the moment APIs change or flags get deprecated. Checkly does it differently. Our SKILL.md is just 100 lines of CLI pointers. No baked-in docs. Your coding agent learns what it needs, when it needs it, straight from the Checkly CLI.

Playwright is too hard, too slow, and too flaky — right? In this webinar, Stefan busts six common end-to-end testing myths and shows how to reuse your Playwright tests as production monitors with Checkly. He covers codegen, trace viewer, UI mode, flakiness root causes (and fixes), and a quick look at Playwright MCP for AI-assisted test generation.

50% of Checkly's CLI users are already coding agents. We predict that agents will become dominant by the end of 2026. This video demonstrates an agentic workflow where an alert reports a broken Shopify store login flow, and Claude Code, using the installed Checkly Skill and the Checkly CLI, pulls monitoring results, identifies a Playwright test failure, investigates the codebase, finds and fixes a bug, and then updates a Checkly status page by creating an incident.

Tangling DNS, TCP handshake failures, packet loss: your network has blind spots that application-level dashboards miss. In this session, Daniel Paulus (VP Engineering, Checkly) sets up DNS, TCP, and ICMP monitors from scratch and deploys them as code using the Checkly CLI. You'll see how to import checks from the UI to a code project, use coding agents to build monitors, and debug network failures with Rocky AI, trace routes, and packet captures.

Checkly introduces ICMP monitoring to complement its existing uptime and synthetic monitoring (URL/HTTP, TCP, DNS, and heartbeat checks) for systems without HTTP endpoints, such as database hosts, VPN gateways, and load balancers.

How does one of the world's leading AI companies keep its infrastructure reliable while shipping new models constantly? In this webinar, Devon Mizelle, Senior SRE at Mistral AI, shares the real story. Devon walks through how Mistral built an automated system that generates synthetic checks for every model the moment it goes live—no manual configuration, no forgotten monitors, no inconsistent alerting. Using monitoring as code, his team eliminated the toil of maintaining hundreds of checks across a rapidly evolving model ecosystem.

We've been running Playwright in production for years. Today, we, at Checkly, are going all in with Playwright Check Suites. Playwright Check Suites is our latest step towards uniting testing and monitoring into a single workflow. It's our biggest advancement yet! Here's why this matters: We're not adapting Playwright anymore. We're running it natively in production with full `playwright.config` support, complete custom dependency control, and support for every tag, spec, or configuration.

Looking to cut your Datadog bill and modernize your monitoring workflow? In this session, Dan Giordano and Giovanni Rago show how to migrate your Datadog synthetic monitors to Checkly in minutes, unlocking Playwright, Monitoring-as-Code, and AI-powered automation. Timestamps: Intro — Why Migrate from Datadog Dan introduces the session, what will be covered, and who it’s for.

Pair Playwright with LLMs to plan, generate, refactor, and monitor end-to-end tests, without shipping hallucinations. This webinar showcases practical workflow: ground models with fresh docs, driving the browser via Playwright MCP, auto-fixing failing tests, refactoring to POMs, add API checks, and reusing the same suite for synthetic monitoring in Checkly. Chapters.

In this webinar, Stefan Judis (Developer Relations at Checkly) and Dan Giordano (VP of Marketing at Checkly) dive into how LLMs and AI tools can be used with application monitoring. You’ll see a live demos of integrating Claude Code, Playwright MCP, and Checkly’s Monitoring as Code. ⸻ Timestamps ⸻ Resources & Next Steps ⸻ Subscribe for more sessions on application reliability, testing, and AI-powered DevOps!