Input Canonicalization
Every human intent must be normalized into a governed Intent Object before processing.
Kernex Deterministic Core Doctrine
Same intent. Same ontology. Same output. Auditable forever.
Kernex determinism is the constitutional rule that governs how human intent becomes structured reality intelligence. The same normalized intent, same stakeholder profile, same domain, same mission, same ontology version, and same constraints must always generate the same decomposition, relationship graph, DISC output, ontology selection, execution blueprint, and audit hash.
Intent Object
Recursive Decomposition
Relationship Intelligence
DISC Compiler
Ontology Registry
Audit Hash
Reproducibility Tests
The nine deterministic laws.
These laws define how Kernex must behave once it moves from public demonstrator to deterministic intelligence kernel.
Ontology Explicitness
No output is valid unless the ontology name, version, hash, and compatibility status are known.
Recursive Stability
Decomposition must follow stable rules, not random generation or uncontrolled AI variance.
Relationship Traceability
Every actor, entity, workflow, control, evidence item, and output must be traceable.
DISC Compilation
All outputs must compile into Domain, Intelligence, System, and Control structures.
Evidence Attachment
Assumptions, sources, risks, controls, approvals, and validation results must follow every output.
Hash Reproducibility
Every deterministic run must produce input, ontology, and output hashes.
Versioned Evolution
If ontology or rule versions change, the output may change, but the difference must be explainable.
Governed Execution
No output moves to AppCloud, workplace provisioning, or production without validation and audit trail.
Deterministic core object model.
The doctrine is implemented through stable objects: Intent Object, Decomposition Object, Relationship Graph, DISC Output, Ontology Selection, and Audit Object.
Intent Object
Immutable input object containing raw intent, normalized intent, stakeholder, domain, mission, constraints, language, and ontology version.
Decomposition Object
Structured output containing domains, entities, actors, events, states, constraints, risks, and controls.
Relationship Graph
Traceable graph connecting actors, responsibilities, workflows, dependencies, feedback loops, and evidence.
DISC Compiler
Compiler layer that converts decomposition into Domain, Intelligence, System, and Control outputs.
Ontology Registry
Versioned catalogue of domain ontologies, compatibility rules, schemas, and dependency maps.
Audit Object
Run-level proof containing run ID, input hash, ontology hash, output hash, reproducibility status, and timestamp.
Interactive deterministic console.
This browser-level console proves the doctrine visually. The next backend phase must convert this exact logic into real APIs and persistent run records.
Run deterministic kernel
Deterministic output
Intent Object
Recursive Decomposition
Relationship Intelligence
DISC Output
Ontology Selection
Audit Object
Backend construction path.
This doctrine page now defines the platform truth. The next engineering step is to implement the backend deterministic kernel behind these contracts.
1. API Kernel
Implement intent capture, decomposition, relationships, DISC compile, ontology selection, workplace provisioning, and audit APIs.
2. Persistence
Store every run object, ontology version, graph edge, output, validation, and audit hash in PostgreSQL.
3. Test Harness
Prove determinism through same-input/same-output, version-difference, schema validation, and unsupported-domain tests.
4. Workplace Binding
Connect /demo-workplace/ to real deterministic runs instead of simulated browser output.