Systems | Development | Analytics | API | Testing

AI has transformed how software is built and tested, yet many teams still rely on traditional metrics like pass rates, coverage, and defect counts. While these numbers look good on dashboards, they often fail to answer the most important question in the AI era. Can we actually trust what the system is doing?

Before building artificial intelligence, it’s worth asking whether we truly understand natural intelligence. Just as early pioneers of flight studied the principles of aerodynamics and observed how birds fly, this session argues that progress in AI requires a deeper understanding of human intelligence and the knowledge that already exists across related disciplines.

Welcome to a new era of intelligent test automation with Katalon TrueTest — a revolutionary AI-powered solution that bridges the gap between manual and automated testing. In this detailed end-to-end walkthrough, Mahtab Siddique, Senior Solutions Architect at Katalon, showcases how TrueTest uses AI and real user behaviour to generate, maintain, and optimise automation tests automatically.

Generative AI can produce tests instantly, but speed alone does not guarantee quality or safety. Without proper validation, AI-written tests can become brittle, redundant, or misleading, creating a false sense of coverage. This session looks at the risks of relying on AI-generated tests without the right controls in place.

Dive into the biggest breakthroughs for the Confluent Connect ecosystem in 2025! This year, we made moving data easier than ever, from modernizing legacy systems with the Oracle XStream CDC Premium Connector to empowering developers with Custom SMTs and Custom Connectors on Google Cloud. Discover the over 10 new connectors we launched, including Snowflake Source, Azure Cosmos DB v2, and Neo4j Sink, plus the release of Confluent Hub 2.0. Learn how Confluent Cloud connectors are breaking down silos and building bridges for your next-gen AI and data modernization projects.

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.