Systems | Development | Analytics | API | Testing

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See Sauce AI for Insights in action and discover how you can eliminate hours of manual work, accelerate release cycles, and ship with confidence. This on-demand demo walks you through our suite of purpose-built AI Agents that transform complex test data and workflows into instant, actionable intelligence. In this video, you will see how Sauce AI.

The rise of AI is transforming how software is developed and tested—but the hardest questions for QA leaders aren’t about tools. They’re about people, productivity, and long-term strategy. As organizations race to integrate AI, leaders must rethink how they hire, train, and guide teams while making tough decisions about whether to build custom solutions, buy off-the-shelf platforms, or take a more measured approach. This panel brings together leaders who are navigating these crossroads in real time.

AI in automation is rapidly emerging as a powerful enabler for testers. From automation script generation utilities to simplifying API testing and framework development, these capabilities promise to accelerate productivity. But as testers embrace assistants like Copilot and Cursor, big questions emerge - What does this mean to the future of open-source frameworks like Selenium and Playwright? How should testers balance the speed of AI-generated code with the need for reliability and maintainability? ⁠How do we measure productivity gains from AI-paired programming? And AI doesn't stop at coding.

In this fireside chat with Jaydeep, we’ll dive into how AI is changing the way we measure success in both QA processes and live generative AI bots. On the QA side, we’ll look at cycle time reduction—the “time goalie” metric that shows how quickly we move from discovering a bug to fixing it. We’ll also talk about predictive quality accuracy, which shifts QA from being reactive to proactive by predicting which code changes are most likely to introduce bugs. And of course, we’ll touch on test creation velocity—how much faster teams are able to create meaningful automation with AI’s support.

Model Context Protocol (MCP) lets AI agents use real tools safely. In this talk, we’ll see how Playwright MCP bridges agents and a real browser by leveraging the accessibility tree (not screenshots) to navigate pages, locate elements, perform actions, and extract data—then export stable Playwright tests you can commit.