The Reference
Architecture / 7 min read
A Layered AI-Native Architecture
Six horizontal architecture layers and two cross-cutting pillars define the AI-native enterprise.
The eight things that make an enterprise AI-native
An AI-native enterprise requires more than models and chat interfaces. It needs a layered architecture that connects engagement, orchestration, agents, models, ontology, enterprise systems, governance and AI operations.
The architecture can be understood as six horizontal layers and two cross-cutting pillars.
Six horizontal layers
- L1 Engagement Layer: customer journeys and workforce AI surfaces.
- L2 Business Orchestration Layer: the transformation canvas where current processes become agent-led departments.
- L3 Agentic Layer: multi-agent choreography, agent patterns, federation and registry.
- L4 Model Hub: curated model portfolio, private/public strategy, sovereignty, evaluation and lifecycle.
- L5 Business Ontology Layer: semantic layer, ontology, decision semantics, policies and glossary.
- L6 Enterprise Open Substrate: services catalog, event mesh, master identity and agent-callable systems.
Two cross-cutting pillars
- Security & Governance: agent identity, prompt firewall, red-team practices, auditability and governance.
- AI Operations: telemetry, evaluation, feedback, FinOps, GreenOps and DevSecOps.
Assess your architecture readiness
Use the Native AI Architecture Diagnostic to identify which layers are solid, emerging or absent.