Hybrid AI architecture: neural, deterministic and verifiable
Compose neural models, retrieval, rules, solvers and verifiers without inferring system safety from component safety.
A hybrid architecture assigns each responsibility to the substrate with the strongest observed affinity for the required function.
Two components that work separately may create unsafe interactions when composed or when they share state.
Component evidence comes first, followed by composition evidence and qualification of the complete system.
What the architecture must make explicit
The engineering object is the complete realization. A model is only one possible component among data, memory, routing, tools, rules, verifiers and runtime.
Move from a keyword to an inspectable engineering decision
Observe
Measure the baseline and bind every behavior to the exact realization that produced it.
Intervene
Test one change at a time to attribute gains and regressions.
Compose
Verify interfaces, shared state and interactions before promotion.
Qualify
Apply hard gates to the frozen realization within a declared scope.
A claim is only as strong as its binding
The gate must evaluate the exact output produced by the exact realization. Integrity, scientific validity and authority are related, but never interchangeable.
Questions that change the design
What does hybrid ai architecture: neural, deterministic and verifiable require?
A hybrid architecture assigns each responsibility to the substrate with the strongest observed affinity for the required function. The engineering object is the complete realization. A model is only one possible component among data, memory, routing, tools, rules, verifiers and runtime.
Which evidence should be retained?
At minimum: exact realization identity, inputs and outputs, protocol version, measurements, failure cases and the decision rule. Component evidence comes first, followed by composition evidence and qualification of the complete system.
Does AMF prescribe a particular model?
No. AMF treats the model, runtime, data, tools and deterministic components as candidate materials. Their retention depends on measured contribution and declared constraints.