Resources

Playbooks, guides, and best practices for AI-native E2E testing.

The Best Acceptance Criteria Read Like Evidence
Most acceptance criteria are too vague to protect a release.
Modular YAML test composition: practical patterns you can reuse for readable, durable automated tests
This post uses example YAML to illustrate modular composition patterns. Exact keys and structure vary by tool. Shiplight AI supports a human-readable YAML format with variables, templates, reusable functions, and modular composition, so the patterns below map cleanly to how teams structure real Ship
Enterprise-Ready Autonomous QA: Shiplight AI Services That Keep Fast Releases Safe
AI-native teams are shipping more code, more often, with fewer human checkpoints in the loop. That velocity is a competitive advantage until the first regression slips through and turns “moving fast” into incident response, hotfixes, and lost trust.
A Test Dashboard Is Not a Scoreboard. It Is a Triage System.
Most live test dashboards fail for the same reason most reporting fails: they describe the build, but they do not guide the next decision.
Pull request driven test generation that actually covers the change
Every team wants the same outcome from automated testing: confidence that the pull request you are about to merge will not break the product. Yet most CI pipelines still rely on one of two blunt instruments:
From brittle checks to real proof: How Shiplight AI assertions validate UI rendering and DOM structure
Most UI test suites fail for the same reason they were written: they try to “prove” an experience using evidence that is too thin.