Architecture at QCon London 2026

Details coming soon.

Details coming soon.

Details coming soon.

Details coming soon.

As organizations move from single-cloud setups to hybrid and multi-cloud strategies, they are under pressure to build data platforms that are both globally available and AI-ready.

Modern AI applications struggle not because of a lack of models, but because meaning is implicit, fragmented, and brittle. In this talk, we’ll explore how making semantics explicit (using ontologies and knowledge graphs) changes how we design, build, and operate AI systems.

As Netflix scales hundreds of client platforms, microservices, and infrastructure components, correlating user experience with system performance has become a hard data problem, not just an observability one.

Netflix didn’t start as a global streaming platform. It began as a US-centric DVD-by-mail business, with a commerce system designed for one country, one currency, and relatively simple payment flows.

As Netflix expanded internationally, those early assumptions began to break.

Over the last decade, streaming architectures have largely been built around topic-centric primitives—logs, streams, and event pipelines—then stitched together with databases, caches, OLAP engines, and (increasingly) new serving systems.

ML training pipelines treat data as static. Teams spend weeks preprocessing datasets into WebDataset or TFRecords, and when they want to experiment with curriculum learning or data mixing, they reprocess everything from scratch.

Senior IC roles are often said to be about influence – but what do we mean by influence? And what are the tools that a senior IC can employ to influence individuals and teams within a large organisation?

Early AI integrations often start small: wrap an inference API, add a prompt, ship a feature. At Zoox, that approach grew into Cortex, a production AI gateway supporting multiple model providers, multiple modalities, and agentic workflows with dozens of tools, serving over 100 internal clients.

Your agent has 100k tokens of context. It still forgets what you told it two messages ago.

Building correct distributed systems takes thinking outside the box, and the fastest way to do that is to think inside a different box. One different box is "formal methods", the discipline of mathematically verifying software and systems.

A “simple” API request rarely stays simple. In distributed systems, one call quickly turns into fan-out across gateways, services, caches, and databases — and your p99 becomes the sum of every hop and every flaky dependency.

AI agents are rapidly changing how users interact with software, yet most agentic systems today operate with little to no intelligent memory, relying instead on brittle context-window heuristics or short-term state.

In Minecraft, every world is built from blocks. At the very bottom lies bedrock: an unbreakable foundation that everything else rests on. Above it sit layers of stone, dirt, sand, and other materials.