OntologiesFeatured
Ontologies for AI Agents
How to define the entities, relationships, and rules an agent reasons over, and how to hand that definition to the agent at runtime.
Guides
Every guide, one architecture
Each guide covers one layer or one decision in depth, and tells you where it connects to the rest of the system.
Available now
- Knowledge GraphsFeatured
Knowledge Graphs for Grounded Retrieval
Designing, populating, and querying a knowledge graph that makes retrieval traceable and lets a model reason across more than one hop.
- AI ArchitectureFeatured
The AI Stack, Layer by Layer
A reference map of the modern AI stack, from data sources through the knowledge layer to models, orchestration, and interfaces.
In progress
These guides are being written. Their outlines are final; the full text ships on the date shown.
- Data ModelingComing soon
Data Modeling for AI Systems
Bringing data modeling discipline to features, documents, chunks, and embeddings so every part of the system agrees on what the data means.
- Intelligent AgentsComing soon
Agent Memory and State
What an agent should remember, for how long, where that memory lives, and how it stays consistent with the knowledge layer.
- AI InfrastructureComing soon
Inference Infrastructure
Serving, routing, caching, and observing model inference so the system stays reliable and the bill stays explainable.
- Intelligent AgentsComing soon
Instructional Design for AI Agents
Applying instructional design to how agents are briefed: objectives, scaffolding, worked examples, and assessment instead of ever-longer prompts.
- OntologiesComing soon
Taxonomies, Vocabularies, and Ontologies
Choosing the right level of structure for a domain, from a flat controlled vocabulary to a formal ontology, and knowing when to stop.