Architecture
Where planning, state, and control live.
The most consequential decision in any AI system isn't which model to use — it's where the intelligence lives. Does the model handle everything, or does the system around it handle planning, memory, and coordination? These terms name the architectural patterns that determine whether AI systems scale, share knowledge, and remain manageable — or collapse under their own complexity.
System-Centric AI
An architecture where planning, state, memory, and control live outside the model.
Model-Centric AI
An architecture where the model is expected to infer state, intent, plan, and memory from raw context.
Tool Isolation
Memory and context don't cross tool boundaries — each tool is a silo.
The Expertise Trap
Context and knowledge locked on one person's machine, inaccessible to the team.
Agent Fragmentation
Agents operating in disconnected silos with no shared state, duplicating work and contradicting each other.
Unified Context
A single, shared knowledge layer accessible to all agents and tools. The cure for Tool Isolation.
Context Portability
The ability to move knowledge between tools, agents, sessions, and team members without loss. The cure for The Expertise Trap.
Stateful, Inspectable Memory
A collaborative memory space where multiple agents and humans contribute to and draw from the same knowledge base — with full history and traceability.
Context Handoff
The ability to transfer full working context from one agent, session, or tool to another without loss. A specific operation enabled by Context Portability.
Intelligent Routing
The system's ability to direct tasks to the right model, tool, or agent based on complexity, cost, and capability — rather than sending everything to the most expensive option.
AI Native
A system, product, or organization designed from the ground up with AI as a first-class architectural component — not bolted on as a feature.