The Semantic Gap

Data Quality in the Agentic AI Era

This gap is manageable when AI advises. It becomes catastrophic when AI acts.

Three Layers of Data Quality

Hover to explore scenarios

✓

Structural Qualitythe pipes

FreshnessCompletenessSchema ValidationPipeline SLAsDeduplication

Covered

Semantic Qualitythe meaning

Shared DefinitionsBusiness RulesOntologyData ContractsLineage

The Gap

✗

Contextual Qualitythe expertise

Domain ExpertiseBusiness ContextIndustry PatternsOperational Judgment

Missing

What enterprises have today → What agentic AI requires

Business GlossaryHuman-interpretable only

VocabularyTerms & Definitions

TaxonomyClassification without relationships

RelationshipsHow Entities Connect

Data ModelStorage schema ≠ business meaning

ContextDomain Knowledge

“A machine-readable rulebook agents can query at runtime.”

Same agent, same data, fundamentally different outcome

Without Semantic Layer

Agent queries raw table: SELECT notional_value FROM trades

System A returns gross: $840M. System B returns net: $500M

Agent computes gap: $340M. No way to know definitions differ

Agent executes $47M hedge against a phantom exposure

$47M loss on a definitional mismatch

With Semantic Layer

Agent queries semantic API: GET /entities/notional_value?context=risk

API resolves: Sys A = gross, Sys B = net. Returns normalized values + confidence

Confidence: 0.42 (below threshold). API flags: definition_conflict detected

Agent escalates to human trader. No action taken. $47M preserved.

Conflict detected. Human decides. $0 loss.

The semantic layer is the governance layer. The organizations that build for this now will be the ones whose agents are trusted to act.