Model-Agnostic Semantic Discovery
by Nick Clark | Published March 27, 2026
Semantic discovery is not coupled to any specific inference engine. The traversal architecture operates with any model that can evaluate semantic content and produce structured assessments. Discovery objects define their inference requirements through capability specifications. The index matches these requirements to available inference resources at each anchor without mandating a specific model architecture.
What It Is
Model-agnostic discovery separates the traversal architecture from the inference engines that evaluate content at each anchor. The discovery object specifies what inference capabilities it needs, not which specific model to use. Anchors provide inference services through whatever engines are available, and the discovery object evaluates results based on structured output rather than model identity.
Why It Matters
Coupling discovery to a specific model creates dependency on that model's availability, capabilities, and licensing. It also prevents the architecture from benefiting from model improvements without architectural changes. Model-agnostic design ensures that discovery can leverage whatever inference capabilities are available, now and in the future, without structural modification.
How It Works
The discovery object's capability requirements specify the type and quality of inference needed at each traversal step. When the object visits an anchor, the anchor matches these requirements against its available inference resources and provides the best available option. The discovery object evaluates the inference quality through structured output assessment rather than model identification.
Different anchors may use different models. The discovery object's traversal remains consistent because it operates on structured outputs, not model-specific formats.
What It Enables
Model-agnostic discovery enables heterogeneous deployments where different regions of the index use different inference engines based on local requirements, cost constraints, or regulatory requirements. It also future-proofs the architecture against model evolution: as better models become available, they can be deployed at any anchor without modifying the discovery infrastructure.
