A structural assessment case is formed when an observed AI-fabric condition is first diagnosed through V1–V4 and then anchored to Application identity, Scenario Class, Metric Set, Regime Classification, and Evidence Interface. The protocol is public and methodological. It does not disclose customer-specific operator selection, telemetry mapping, scoring, weighting, production thresholds, intervention playbooks, or runtime integration logic.
Structural diagnostics and structural assessment are sequential but distinct operations. Diagnostics identifies the structural problem form: what kind of condition is present inside the SORT-AI architecture. Assessment asks the next question: can that condition be made structurally assessable under a declared scenario, metric set, regime classification, and evidence interface? Without diagnostic anchoring, an assessment case has no stable identity. Without assessment structure, a diagnosis remains descriptive and cannot connect to downstream evidence.
Diagnostics = identification of a structural problem form
Assessment = structured evaluation of scenario, metric, and regime behavior
The system condition is read as a recurrent structural form inside the SORT-AI domain architecture, for example coupling instability, runtime-control incoherence, or agentic amplification.
The condition is anchored to Application identity, Scenario Class, Metric Set, Regime Classification, and Evidence Interface.
The public protocol defines the assessment grammar, not a production assessment engine, telemetry pipeline, scoring function, or intervention playbook.
The V1–V4 grammar is an epistemic ordering convention for assessment preparation. It is not a mechanistic causal chain, not a dynamical model, and not a production diagnostic algorithm. Its role is to order an observed condition so that the condition can be translated into an assessment case.
What becomes visible at the system level. Examples include degraded effective throughput, rising cost per useful output, unstable tail latency, retry amplification, benchmark-deployment divergence, or auditability gaps.
The structural relation that makes the observation intelligible. Examples include scheduler-runtime interaction, accelerator placement, memory-path coupling, orchestration feedback, retry logic, policy enforcement, agentic tool-use loops, or evidence-surface fragmentation.
The structural effect class that appears once the coupling relation is read. Examples include control-coherence loss, retry amplification, interconnect-induced capacity loss, agentic divergence, evaluation-deployment projection mismatch, or evidence incompleteness.
The surface on which the condition becomes assessable, actionable, or decision-relevant. This may include regime classification, evidence-interface compatibility, architecture decision support, governance readiness, or runtime-control interpretation.
Observation → V1 → V2 → V3 → V4 → Application Identity → Scenario Class → Metric Set → Regime Classification → Evidence Interface
Formal assessment-case tuple:
A_case = (S_D, V1, V2, V3, V4, A_j, C_jℓ, M_jℓ, ρ_jℓ, E_jℓ)
The first part of the chain is diagnostic: Observation → V1 → V2 → V3 → V4. The second part is assessment-oriented: Application Identity → Scenario Class → Metric Set → Regime Classification → Evidence Interface. Together they define a public analysis-layer object. The tuple is interpretive, not executable.
S_D → Ĵ_D → P̂κ(Ĵ_D) → R_D(Δ)
Ĵ_D is an abstract structural coupling chain.P̂κ is a kernel-modulated projection.R_D(Δ) is a structural deviation or risk field.Customer-specific operator chains are not part of the public analysis layer.
A scenario class becomes evidence-compatible when its metric set admits declared baseline/comparison risk pairs. For kernel-damping evidence releases, these pairs are transformed into risk-transition vectors and tested through the public evidence protocol.
The full methodological protocol is described in the Technical Note “A V1–V4 Diagnostic Protocol for SORT-AI Structural Assessment: From AI-Fabric Observation to Scenario-Class Evidence Interfaces.”