Systems | Development | Analytics | API | Testing

Most AI failures aren’t model problems. They’re data pipeline problems. Disconnected systems. Inconsistent preparation. No governance at query time. This short animation walks through the 5 Pillars of AI-Ready Data and shows how data needs to move through a structured pipeline before it can power reliable AI. 5 Pillars of AI-Ready Data Access → Prep → Context → Governance → Monitoring Five stages. One connected flow.

Is your AI agent one misconfigured server away from a production data leak? In this deep dive, Jeremy from Lenses explores the critical security architecture of the Model Context Protocol (MCP) and how it’s evolving to protect the future of Agentic Engineering.

Discover the powerful new IDE-like Studio in Lenses 6.2. Learn how to manage your Kafka clusters, discover topics across multiple environments, and perform side-by-side comparisons of dev and staging data. We also dive into the new ways to interact with streaming data, including the CLI, VS Code plugin, and the new MCP server for AI agents and chatbots. Whether you're a developer troubleshooting schema mismatches or a data engineer managing complex Kafka estates, the new Lenses Studio provides the tools you need to stay in context and work efficiently.

Where agentic AI meets your analytics stack to drive action at scale. The shift is here. As the industry moves from Generative AI (Chat) to Agentic AI (Action), the pressure is on for developers and data practitioners to design intelligent apps that don't just talk: they perform. The real challenge? Bridging the gap between sophisticated developer tooling and your enterprise analytics stack. That’s exactly what this session solves.

NKT makes the “electrical superhighways” that bring renewable energy to city consumers. Its 24/7 site in Karlskrona, Sweden is the world’s largest producer of high voltage undersea cables, making operational stability vital. However, the plant faced hurdles. Data was trapped in silos, leading to intuition-based decisions with no single source of truth.

Learn more: Speedscale.com.

The tsunami of AI-generated code creates downstream bottlenecks for QA teams, and shift-left or traditional test automation aren't enough in the AI era. In this From the Bear Cave session, SmartBear CEO Dan Faulkner and CMO Kelly Wenzel unpack how AI code generation impacts software quality and why traditional testing struggles to keep up.