Architecture is where AI becomes a system.
Data
Where information enters.
Knowledge
How information becomes structured and usable.
Retrieval
How the system finds relevant context.
Intelligence
Where reasoning, generation, prediction and decisions happen.
Memory
What the system retains over time.
Tools
What the intelligence can actually do.
Evaluation
How behavior is measured.
Infrastructure
Where the system operates.
01Separate intelligence from interface.
02Don’t confuse retrieval with knowledge.
03Don’t give agents unnecessary autonomy.
04Evaluate the system, not just the model.
05Design failure states before deployment.
06Build for iteration.
01Simple AI application
02RAG system
03Agent system
04Multi-agent system
05Persistent intelligence system
06Knowledge graph system
07Custom model system
08Enterprise AI architecture
The feature is what users see. Architecture is what makes it survive.
Projects may look different. The underlying architecture often isn't.
Retrieval
Memory
Agent Orchestration
Evaluation
Knowledge Engineering
Model Routing
ARCHITECTURE PIPELINE
A deliberate path from data to feedback.
Data Ingestion
Multiple data sources unified into structured format.
Draft Brain Curation
Knowledge organization before model training begins.
Model Training
Custom architectures learning from organized data.
Validation
Rigorous testing against real-world constraints.
Inference & Feedback
Continuous learning through production feedback loops.
Each stage is deliberately sequential. We do not skip problem understanding. We do not rush to LLM integration. Intelligence is designed, not defaulted.