How do you take a non-deterministic Large Language Model and turn it into a reliable, production-ready system? The answer isn't more prompts, it's AI Engineering. QCon London 2026 track host Hien Luu, Sr. Engineering Manager @Zoox, defines the mission for the AI Engineering track: establishing the rigorous engineering practices required to build, deploy, and maintain AI-powered applications. The track focuses on three pillars of the AI lifecycle: 🔹 Design patterns: Moving beyond basic implement
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Daniel Bryant, QCon London 2026 Track Host, is excited about the Model Context Protocol (MCP) because it brings a familiar engineering discipline to the often unpredictable world of AI. Why this matters for your architecture: 🔹An "Unlocking Function": MCP isn't just another buzzword. It is the key to how different AI systems will finally "talk" to each other and your data 🔹Another way of thinking about APIs: If you know how to build a good API, you already have the skills for MCP. The same pri
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