Data Product as backbone of AI

#networking #architecture #business #AI #context #VirtualEvent

Large language models are increasingly commoditized, yet enterprise AI agents built on them routinely fail to produce accurate, trustworthy, or auditable outputs. This session argues that the missing piece is not a better model but a missing architectural layer: the Context Layer. A three-tier framework for AI-readiness will be introduced — a governed data foundation, a semantic context layer, and the agents themselves — showing that the majority of the effort required to stand up a reliable enterprise agent lies in the middle layer, not in the agent or its integrations.

The talk will discuss a reference architecture for this Context Layer, comprising a semantic repository (ontologies, business glossaries, certified metrics, and persona-aware dynamic context), a governance workflow for authoring, certifying, and publishing definitions with full lineage and drift detection, and a single open interface through which every downstream tool and agent discovers and consumes certified context. A five-step reasoning pipeline — interpret, decompose, match, apply context, and assemble — will be explained, showing how natural-language questions are grounded in this governed model before any query executes, with role- and attribute-based access control and compliance guardrails enforced on every answer.