Speaker
Abstract
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. Drawing on real work from the GoingMeta.live podcast series, we’ll look at how ontologies move from theory to practice: grounding LLMs, improving reliability, enabling reasoning, and creating shared understanding across teams and systems. Expect concrete patterns, lessons learned, and examples you can apply to production AI today.
QCon London 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Architecture in the Age of AI Hosted by Fabiane Nardon Data Expert, Java Champion & Data Platform Director @totvsFrom the same track
Wednesday 18 March
10:35 Fleming (3rd Fl.) Session agentic coding The Right 300 Tokens Beat 100k Noisy Ones: The Architecture of Context Engineering Patrick Debois, Baruch Sadogursky Your agent has 100k tokens of context. It still forgets what you told it two messages ago. 11:45 Fleming (3rd Fl.) Session AI/ML Beyond Benchmarks: How Evaluations Ensure Safety at Scale in LLM Applications Clara Matos Director of Applied AI @Sword Health, Focused on Building and Scaling Machine Learning Systems As LLM systems move from prototypes to production, the gap between benchmark performance and real-world reliability becomes impossible to ignore. Models that score well on benchmarks can still fail unpredictably when facing the complexity, ambiguity, and edge cases of real users. 13:35 Whittle (3rd Fl.) Session data platform engineering Building an AI Ready Global Scale Data Platform George Peter Hantzaras Engineering Director, Core Platforms @MongoDB, Open Source Ambassador, Published Author 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. 14:45 Whittle (3rd Fl.) Session Your Agent Sandbox Doesn't Know My Authz Model: A Standard-Shaped Hole Paul Carleton Member of Technical Staff @Anthropic, Core Maintainer of MCP Sandboxes are the first line of defence for agentic systems: restrict the bash commands, filter the URLs, lock down the filesystem. But sandboxes operate on the syntax of requests, not the semantics of your authorization model. 15:55 Whittle (3rd Fl.) Session Explicit Semantics for AI Applications: Ontologies in Practice Jesús Barrasa Field CTO for AI @Neo4j 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.