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Platform Layers › L4 Intelligence
L4 Intelligence
Where this layer sits on the architecture; the client plane band to its right shows the values it reads.
L4 Intelligence (the AI layer) VENDOR-AGNOSTIC · SWAPPABLE
The reasoning engines: foundation LLMs, small language models, embeddings, rerankers, and the model router that forms the portability boundary. Reasoning is rented, not owned.
Model router isolates every vendor and version specific, a swap is a one-file change
Model mix (foundation vs small models) optimized by the operational loop, within plane cost ceilings
Regression evals gate every model version migration
This thin layer absorbs model progress: models upgrade, nothing above changes, AGI-readiness is a one-layer swap, not a rebuild
Boundary test: no knowledge stored here, no business logic, nothing above this layer names a vendor.
Plane parameters: model tier · cost ceilings
Client-specific configuration for this layer is specified band by band in Client Delivery, C4 spec: every input with its binding key, generated artifact, and verifying check.
Component reference
Model Router
The portability boundary. Routing policy is coded by P1Z; transport is configured on the cloud.
ImplementationA versioned routing table shipped through CI holds vendor credentials, version pins, fallback chains and A/B routes. A model swap is a one-file change; the data-sensitivity boundary (C4) is enforced at this hop.
Foundation LLMs
Frontier models for judgment work: synthesis, drafting, interpretation.
ImplementationReached only through the router; nothing above this layer names a vendor, and regression evals gate every version migration.
Small Language Models
Cheap, narrow calls: packaging, formatting, extraction, short narratives.
ImplementationTier selection comes from routing policy within the plane's cost ceilings; the Agent Hammer antipattern is what this tier exists to prevent.
Embeddings
Vector representations powering retrieval.
ImplementationServed behind the same boundary, version pinned; re-embedding is a migration project with regression evals, never a casual upgrade.
Rerankers
Reorder retrieved candidates by relevance before they reach an agent's context.
ImplementationSame boundary; measured by retrieval-quality metrics inside the eval suites.