Systems | Development | Analytics | API | Testing

Agentic code generation has dramatically accelerated development, but QA often remains slowed by coordination gaps. Manual planning, delayed handoffs, and fragmented reviews continue to create friction, while AI agents operate in isolation without orchestration or governance. This session explores how to compose these siloed agents into a unified, AI-powered STLC where workflows are dependency-aware, auditable, and automatically triggered as soon as prerequisites are met.

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.

Katalon supports the full testing lifecycle by using Studio to create tests, TrueTest to generate additional user-based tests, and TestOps to manage everything: from organizing, planning, and executing tests to analyzing results and tracking defects. Together, they ensure tests scale across teams and projects. — Alex Martins, VP of Strategy at Katalon Follow Katalon for more insights in our series!

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.

In this episode of The AI Forecast, Anu Jain, founder and CEO of Nexus Cognitive, joins host Paul Muller to introduce a transformative idea: AI doesn’t have a last mile problem. It has a first mile problem. While AI models and algorithms can scale instantly through the cloud, their success still depends on the quality, provenance, and readiness of the data that feeds them.

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.

Message brokers are a critical component of modern distributed systems, facilitating asynchronous communication between services. Load testing message broker integrations requires special considerations since the interaction patterns differ from traditional HTTP-based APIs. Speedscale provides specialized tooling to help you load test applications that integrate with message brokers by.

In machine learning, an 85% accurate model is often considered a success because we accept that data is messy, the real world is unpredictable, and chasing perfection is rarely worth the cost. However, in software testing, especially in the GenAI era, the question of “Can we test 100%?” still comes up. With AI systems producing probabilistic outputs where the same input can lead to different results, absolute coverage is unrealistic. Confidence scores already tell us that uncertainty is part of the system, and testing needs to acknowledge that reality.