Your software factory stops at deploy
Most people investing in software factories fail to close the loop. Agents write, review, test and ship code, but then the factory loop ends. What happens after deploy? Nobody knows.
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Prove Your SLAs: How Yext Ties Synthetic Monitoring to SLOs with Checkly (Live-Webinar)
Yext uses synthetic monitoring to prove and meet SLAs, by tying Checkly checks directly to SLOs and user-facing SLIs. Stefan (Developer Relations), Braxton (Solutions), and Shikhar (Engineering Productivity at Yext) cover the SLA/SLO/SLI basics, the math behind "all these nines", and how Yext turns those targets into concrete checks with monitoring as code.
How Agents Rewrote a Node.js Service in Go (With a 600-Test Harness)
Stefan sits down with Eddie, senior engineer at Checkly, to talk through how an AI agent rewrote a legacy Node.js service in Go, and what it actually took to ship that to production safely.
What is synthetic monitoring? Checkly explained in 2 minutes
Learn how Checkly does synthetic monitoring with real browsers to run your critical flows on a schedule. Get alerted with screenshots and traces s when something breaks, and define the whole setup as code in your repo. Chapters.
We Let AI Agents Rewrite a 92M-Message-a-Day Service in Go. Zero Incidents.
Our Results Daemon processes about 92 million messages a day. We recently rewrote it from Node.js to Go, and we let Claude Code write it. We wanted to know whether we could trust an agentic rewrite for a critical, high-throughput production service rather than a prototype. It shipped with zero incidents, a 70% reduction in running pods, and a lighter database load.
Let AI Agents Own Your Monitoring: From Setup to Auto-Fixed Incidents
Your AI agent can set up your monitoring, report on platform health, and open a PR to fix a failing check. All this works automatically. In this webinar, Stefan (Developer Relations at Checkly, Google Developer Expert & Playwright Ambassador) demonstrates how Monitoring as Code makes Checkly agent-friendly by design, and walks through three agentic workflows live.
CLIs are more token-efficient than MCP. Or are they?
MCP servers have a reputation: they eat your context window. CLIs paired with skills, on the other hand, are more token efficient. But is this still true? I dropped all my MCP servers five months ago. Five months is a long time in AI land. When Anthropic came up with the concept of skills, many people stopped using MCP servers in favor of CLI tooling and skills.
Your AI Agent Can Now Debug Playwright Tests
Playwright 1.59 added a `cli` parameter to the `--debug` flag, and this enables your coding agent to use the built-in step-through debugger. Step through the test steps, inspect network traffic at every moment, and access the accessibility tree. The same debugger is now usable without the inspector window.
Playwright MCP vs CLI: the token problem is gone
"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.
New in Playwright: Your Agent Can Now Read Traces
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.