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Modern web applications demand robust end-to-end test coverage, yet maintaining Playwright test suites at scale remains a persistent engineering challenge. Flaky tests, selector drift, and timing issues can consume significant developer time—time better spent on feature <a href="http://development.In" rel="nofollow" target="_blank">development.In</a> this talk, I'll share how we built a fully autonomous test healing pipeline that transforms how our team handles E2E test failures. Our system leverages the Model Context Protocol (MCP) to create an intelligent agent that doesn't just identify failures—it fixes them.What you'll learn:<ul><li>The Architecture: How Playwright Test Healer integrates with MCP to provide AI agents with deep test context, browser automation capabilities, and codebase awareness</li></ul><ul><li>CI/CD Integration: Our approach to embedding the healing workflow seamlessly into GitHub Actions, including failure detection, triage, and automated fix generation</li></ul><ul><li>Developer Experience: How we simplified the entire workflow to a single slash command—/fix-playwright-test <test_file>—that triggers autonomous debugging, generates fixes, and commits suggestions directly to your PR</li></ul><ul><li>Real-World Impact: Quantitative results showing reduced mean-time-to-recovery, increased test stability, and reclaimed developer hours</li></ul>The result? A paradigm shift from reactive debugging to proactive test maintenance. When tests fail in CI, an AI agent analyzes the failure, navigates the application, identifies the root cause, and proposes fixes—all without human intervention.This isn't just about fixing broken tests; it's about reimagining developer productivity in the era of AI-assisted development.
