Systems | Development | Analytics | API | Testing

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.

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.

Learn more at speedscale.com.

Ever tried searching your entire production stack for one user? Getting paged at 3 AM is bad enough. It’s worse when you only have a single username and zero visibility into what’s actually happening across your microservices. With Speedscale, you can perform full-text searches across every API call and database interaction in real-time. Stop guessing and start debugging with total context.

gRPC is fast and efficient - until it breaks at 2:00 AM. Traditional observability tools are built for HTTP/1.1 and JSON. When you switch to gRPC, you’re dealing with binary Protobuf payloads and HTTP/2 multiplexing that most logs and traces simply weren't designed to handle. Speedscale flips the switch by decoding Protobuf directly into human-readable JSON in real-time. Get the speed of gRPC with the visibility of REST.

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.

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.

Many customers were struggling with BDD-style test processes — writing structured, English-language scripts that business and tech teams could agree on but couldn’t easily automate. Before Katalon, they lacked a smooth way to turn those scripts or raw requirements into automated tests, which Katalon (and AI features like Studio Assist) finally made efficient. — Coty Rosenblath, Chief Technology Officer at Katalon.