Operations | Monitoring | ITSM | DevOps | Cloud

"MCP burns too many tokens" is old news. Lazy MCP tool loading and page snapshots on disk fixed the token problem. We benchmarked Playwright MCP vs the CLI, and they're roughly the same. Pick your tools based on workflow, not token anxiety.

Since Playwright 1.59, the new "trace" command makes your test traces fully accessible in the terminal. All plain text, and your agent will love it. And if you're running Playwright with Checkly, our AI agent, Rocky, already reads your traces and delivers root-cause analysis before you even start digging.

An AI agent that can't open a browser is just guessing. Stefan from Checkly shows how giving AI coding agents a real browser via Playwright enables reliable end-to-end test generation and debugging, closing the quality gap created by faster, AI-driven shipping. The session compares Playwright MCP vs the Playwright CLI for agent workflows, showing that thanks to MCP spec changes, lazy tool loading, and skills, the two are now effectively just different interfaces to the same tool, with no real token advantage either way.

Your UI changes, but your monitoring shouldn't break. Checkly's new agentic checks test the outcome you care about instead of running hard-coded steps. The main question is, "Can a user still do X?".

We at Checkly shipped a major CLI v8 release. Here's everything new, and why it's built for your agent. The CLI v8 expands Monitoring as Code and streamlines your everyday workflows: Upgrade and try it: npm install checkly@latest Chapters.

The new checkly api command is an authenticated pass-through to all 100+ Checkly API endpoints. Give it a path, and it handles your token and base URL. Full API coverage for your agents, no wrappers required.

Alerts tell you something broke, but not why. So you're stuck digging through logs and trace IDs. Checkly monitors your app from the outside, like a real user. And Rocky, the Checkly agent, automatically pulls the right context to provide a root cause analysis for any failed check.

Your Playwright tests catch bugs. The hard part is figuring out what actually broke — and sharing that context with your team. This session shows exactly how the Checkly Playwright Reporter solves that: one shared home for all your test runs, AI-powered root cause analysis, and a direct path from failing test to production monitor. María de Antón, PM for Playwright features at Checkly, runs a live demo on a real app with real failures.

Not everything worth monitoring is on the public internet. In this 30-minute hands-on session, Daniel Paulus deploys four Checkly private location agents on AWS EKS with Terraform, then uses a coding agent to scaffold 200 internal checks in seconds — uptime, TCP, DNS, ICMP, and Playwright browser checks against legacy apps that never leave the firewall.

Coding agents are the fastest-growing audience for the Checkly CLI, and we're doubling down on them. In this session, Stefan hands Claude a real e-commerce app, lets it set up monitoring with `npx checkly init`, generate Playwright tests through MCP, and walk an actual alert end-to-end with Rocky AI in the loop.