End-to-end tests are powerful, but they’re slow to write and easy to drift. We translate requirements into test code by hand, and over time the intent gets lost. What if behavior could be described in plain language and turned into reliable E2E coverage through a repeatable playbook?
As an architect working across teams, I’ve been building structured playbooks that AI agents run through the Playwright CLI. These playbooks take requirements, designs, or existing code and generate natural language-based E2E scenarios tied to real user flows. The key isn’t automation. It’s structure. The playbook defines how context is gathered, how scenarios are created, and how results are validated so the system stays predictable.
Join me to see how this works in practice. I will walk through the playbook design, the tradeoffs, and where AI helps versus where human judgment still matters. You will leave with a practical approach to adding AI-driven E2E testing without losing control of your test suite.
This talk has been presented at AI Coding Summit NYC, check out the latest edition of this Tech Conference.
