Advanced Web Testing with Playwright and AI [Testμ 2026]

Andrew Knight’s Testμ Conf 2026 workshop argues that AI can generate reliable Playwright tests when it is supported by structured processes, project rules, and accessible specifications rather than one-off prompts. He presents a “plan, generate, heal” workflow in which agents explore applications and requirements to create test plans, produce executable tests, and repair failures only after human review confirms they do not reveal genuine defects. The session explains Playwright’s performance advantages over Selenium, including browser-level protocols, shared browser instances, isolated browser contexts, automatic waiting, and API support for efficient test setup. Knight distinguishes AI components such as coding agents, models, rules, sub-agents, skills, MCP, and CLIs, emphasizing context engineering and spec-driven development to give agents architecture, standards, domain knowledge, acceptance criteria, and intended business outcomes. Recommended practices include data-testid attributes, page objects, fixtures, atomic tests, parallel execution, efficient API-based data preparation, and treating CI pipelines as production systems. In a live demonstration, an agent generated 13 passing login tests from a single plan using the prescribed project conventions, illustrating both the potential speed gains and the continuing need for human judgment over test coverage, business relevance, and generated output.

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