AI Architecture & Model Layer
Why Agentic AI Needs More Than Intelligence: Knowledge Graphs & Ontologies as the Foundation That Scales
- Why LLMs fail on enterprise knowledge, and why better models, bigger context windows and more MCP servers don't fix it
- Derived vs. decided: the knowledge that makes your company your company is written down nowhere a model has read
- Why writing rules down is not enough. From fairy tales to a $5 million comma to your discount policy
- What "formal" really means: the spectrum from prose to YAML to code to OWL, and where each approach stops working at scale
- BI semantic layers, data catalogs, property graphs, vendor platforms: an honest comparison against five requirements for acting AI
- How a knowledge layer sits between your agents and your systems: ontology, knowledge graph, rules, one point of enforcement
- What to take home: five questions to ask any vendor, do's and don'ts, and how to start without a two-year enterprise project
