Infrastructure & Architecture

Foundation & Build — the physical and logical base that everything else in an AI system runs on.

Role of this domain

Infrastructure & Architecture is the Foundation of the five-domain framework. No matter how good your prompting or orchestration strategy is, the whole system becomes unstable if the infrastructure underneath it is weak.

    flowchart LR
    A["🏗 Infrastructure<br/>Foundation"] --> B["⚙️ Orchestration<br/>Engine"]
    B --> C["🤝 Interface<br/>Interaction"]
    B --> D["📊 Business Impact<br/>Value"]
    E["🛡 Governance<br/>Control"] --> B

    style A fill:#2563EB,stroke:#1D4ED8,color:#fff
    style B fill:#7C3AED,stroke:#6D28D9,color:#fff
    style C fill:#16A34A,stroke:#15803D,color:#fff
    style D fill:#EA580C,stroke:#C2410C,color:#fff
    style E fill:#0891B2,stroke:#0E7490,color:#fff

Core components

Component	Description
Compute resources	GPU/NPU servers, cloud infrastructure optimization
Model selection & tuning	Choosing the right LLM for the job, fine-tuning, quantization
AI model benchmarking	Quantitative analysis of intelligence, speed, and price based on Artificial Analysis
Frontier benchmarks & evaluation	What ARC-AGI, HLE, SWE-bench and agentic suites measure, and how to read a leaderboard
Data pipelines	Real-time data collection and cleaning for AI training and inference
Vector DB	Optimizing vector databases for semantic search, and the store behind agent episodic memory
MCP servers	Context management based on the Model Context Protocol

Core strategy: the model mix

The goal is not simply to “own” models — it’s to run a model mix strategy matched to specific purposes.

Large models: complex reasoning, creative work
Small models: fast responses, cost-efficient classification and summarization
Specialized models: domain-specific tasks such as code generation, image understanding, and speech processing

Health check questions

“Does our infrastructure layer run a model-mix strategy that actually fits its purposes?”

Is GPU/cloud spend optimized within budget?
Does vector DB response time meet SLA in production?
Have we chosen the right strategy among fine-tuning, prompt engineering, and RAG?
Do we evaluate model changes against our own eval set rather than against public leaderboards?
Are MCP servers providing context reliably?
Do the stores holding agent memory have a retention and deletion policy, or do they only grow?

Compute Resource Management

Model Selection & Tuning

AI Model Benchmarking

Frontier Benchmarks & Evaluation Trends

Data Pipelines

Vector DB Optimization

MCP Server Management

AI Model Fundamentals