Operations | Monitoring | ITSM | DevOps | Cloud

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.

Rocky, Checkly's AI agent, monitors production sites and provides an analysis for every failing check. Previously, a coding agent couldn't access this analysis, leaving incidents and agents disconnected. Now, you can access all the analyses via the Checkly CLI (or API) and tell your coding agent, "Hey, I got a Checkly alert. Please investigate!" With Rocky's structured analysis delivered inline, the coding agent can start with a strong hypothesis, fix issues, and propose a PR in one session.

Learn how the Checkly CLI uses a single function (`detectOperator()`) to detect whether the caller is a human, CI, or a coding agent by checking agent-set environment variables. This detection then changes how commands behave to provide the best agent experience.

Building Agent-Friendly CLIs: Why Your AI Agent Already Loves the Checkly CLI Stefan explains why products, docs, and CLIs must be AI-ready as coding agents rapidly become primary users of the Checkly CLI. He outlines key CLI features for agent workflows: Stefan demos how an agent initializes project-tailored Checkly setup from scratch without any human intervention and also shows how agents can entirely automate the incident life cylce from resolution to status page communication.

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.