Playwright + AI: Reimagining Test Automation for the Agentic Era

In this session, Aman Choudhary, Senior Software Test Engineer at Ksolves India Limited, addresses a shift that is already changing how the best QA teams operate: test automation is no longer just about writing scripts faster. It is about building intelligent workflows that can generate, execute, maintain, and improve tests with AI assistance, reducing the manual burden on engineers without sacrificing coverage or reliability.

Most test automation strategies today are still built around the assumption that a human writes every test case, maintains every selector, and reviews every failure manually. That approach does not scale well as applications grow in complexity, and it creates a constant backlog of automation debt that teams never fully catch up on. The result is slower release cycles, inconsistent coverage, and QA becoming a bottleneck rather than an enabler.

This webinar demonstrates how Playwright, AI, Claude Code, and the Model Context Protocol are being combined to build a genuinely different kind of QA pipeline. Aman walks through how AI can accelerate Playwright test creation by generating test cases directly from requirements, how Page Object Models can be built and maintained with AI assistance to stay resilient as UIs change, how Claude Code can be used for test generation, debugging, and automated code reviews, and how MCP connects AI agents to live browser testing environments for end-to-end automation. The session also covers how to bring all of these pieces together into a complete AI-powered QA pipeline that integrates with Jira, Git, Playwright, and MCP, along with the best practices teams need to keep that pipeline production-ready and reliable.

Whether you are a QA engineer, automation tester, or engineering leader, this session provides practical, implementation-focused insights into the future of AI-powered software testing.

Key Takeaways

• How to accelerate Playwright test automation using AI-generated test cases from requirements.
  
• Building maintainable and resilient Page Object Models with AI assistance.
  
• Using Claude Code for test generation, debugging, and automated code reviews in QA workflows.
  
• Connecting AI agents to live browser testing environments using the Model Context Protocol.
  
• Creating an end-to-end AI-powered QA pipeline integrating Jira, Git, Playwright, and MCP.
  
• Best practices for building reliable, production-ready AI-assisted testing pipelines.